Wsns Unique Characteristics And Constrains

Published Date: 02 Nov 2017

CHAPTER 1

Wireless Sensor Network (WSN) is a network of large numbers – up to thousands – of tiny spatially distributed radio-equipped sensors called as nodes or smart dust or motes. Each node in a sensor network is composed of a radio-transducer, a small microcontroller and a long lasting battery for energy source. The sensor node not only sense but also processes the data to make it meaningful by using its embedded microprocessors and also communicates those data through its transceiver. They communicate over a short distance via a wireless medium and collaborate to accomplish a common task. They are used for gathering information needed by smart environments and are particularly useful in unattended situations where terrain, climate and other environmental constraints may hinder in the deployment of wired/conventional networks. An individual node failure is not an issue because of the large scale deployment of these nodes and normally the target area is monitored by several nodes. Primarily these sensors are used for data acquisition and are required to disseminate the acquired parameters to special nodes called sinks or base-stations over the wireless link as shown in figure 1.1. The base-station or sink collects data from all the nodes, and then analyzes this data to draw conclusions about the on-going activity in the area of interest. Sinks or base- stations being powerful data processors can act as gateways to other existing communications infrastructure or to the Internet where a user can have access to the reported data.

Fig. 1.1: Sensor Network Architecture

WSN’s unique Characteristics and constrains:

Dense node deployment: sensor nodes are usually densely deployed in a field of interest. The number of sensor nodes in a sensor network can be several orders of magnitude higher than that in a MANET.

Battery-powered sensor nodes: sensor nodes are usually powered by battery. In most situations, they are deployed in a harsh or hostile environment, where it is very difficult or even impossible to change or recharge the batteries.

Energy, computation, and storage constraints: sensor nodes are highly limited in energy, computation, and storage capacities.

Self-configurable: sensor nodes are usually randomly deployed without careful planning and engineering. Once deployed, sensor nodes have to autonomously configure themselves into a communication network.

Application specific: sensor networks are application specific. A network is usually designed and deployed for a specific application. The design requirements of a network change with its application.

Unreliable sensor nodes: sensor nodes are usually deployed in harsh or hostile environments and operate without attendance. They are prone to physical damages or failures.

Frequent topology change: network topology changes frequently due to node failure, damage, addition, energy depletion, or channel fading.

No global identification: due to the large number of sensor nodes, it is usually not possible to build a global addressing scheme for a sensor network because it would introduce a high overhead for the identification.

Many-to-one traffic pattern: in most sensor network applications, the data sensed by sensor nodes flow from multiple source sensor nodes to a particular sink, exhibiting a many-to-one traffic pattern.

Data redundancy: in most sensor network applications, sensor nodes are densely deployed in a region of interest and collaborate to accomplish a common sensing task. Thus, the data sensed by multiple sensor nodes typically have a certain level of correlation or redundancy.

1.2 APPLICATIONS OF WIRELESS SENSOR NETWORK

Wide area monitoring for personnel / vehicles

Secure area intrusion monitoring and denial

Environmental monitoring

Animal habitats

Migration

Forest fires

Natural disasters

Subsea monitoring

Environmental toxin detection

Building monitoring

Urban area environmental monitoring

Sensors on buildings

Sensors in taxis or buses

Vehicle traffic monitoring & control

Sensors on roadways and traffic lights

Sensors on vehicles

Remote site power substation monitoring

Remote site patient medical monitoring

Smart home

Inventory management

1.3 PERFORMANCE OF WSN IS BASED ON THE FOLLOWING FACTORS:

1.3.1 Latency/Delay: Latency is defined by how much time a node takes to sense, or monitor and communicate the activity. It also depends on the application at hand. Sensor nodes collect information, process it and send it to the destination. Latency in a network is calculated based on these activities as well as how much time a sensor takes to forward the data in heavy load traffic or in a low density network.

1.3.2 Scalability: Scalability is an important factor in wireless sensor networks. A network area is not always static, it changes depending upon the user requirements. All the nodes in the network area must be scalable or able to adjust themselves to the changes in the network structure depending upon the user.

1.3.3 Energy Awareness: Every node uses some energy for activities like sensing, processing, storage and transmission. A node in the network should know how much energy will be utilized to perform a new task that is submitted, the amount of energy that is dissipated can vary from high, moderate to low depending upon the type of functionality or activity it has to perform.

1.3.4 Node Processing Time: It refers to the time taken by the node in the network for performing all the operation starting from the sensing activity to processing the data or storing data within the buffers and transmitting or receiving it over the network.

1.3.5 Transmission Scheme: Sensor nodes which collect the data transmit it to the sink or the base station either using the flat or in multi hop routing schemes.

1.3.6 Network Power Usage: All the sensor nodes in the network use a certain amount of network power which helps them to perform certain activities like sensing or processing or even forming groups within the network area. The amount of energy or power utilized by the sensor nodes or a group of sensors within the network is known as network power usage.

1.3.7 Contention Based or Contention free Protocols: MAC protocols are divided into two groups contention-based and contention-free. In the contention-based group, the protocol allows the multiple nodes to access the single channel. Each node has to sense the medium before sending the data. Collision can occur frequently, and retransmission is required. IN contention-free protocols, on the other hand, the channel is divided into time slots. Each node uses the time slot to send the data. It provides collision free communication because each node knows in advance about the time slots.

1.3.8 Synchronization: When sensors nodes in a network ensure that the receiving end can recognize the data that is transmitted at the other end in the exact order it is sent, this is known as synchronization between two nodes where the flow of data and receiving is done at the same rate. The node needs to have same notion of time in order to go to sleep and wake up at the same time.

1.3.9 Control Packet: A packet which is sent before the transmission between two nodes is known as control packet. Control packet contains the number of data bits sent, the address of the destination node and certain flags which can avoid collisions during transmission.

1.4 ISSUES IN WSN

1.4.1 Limited energy capacity: Since sensor nodes are battery powered, they have limited energy capacity. Energy poses a big challenge for network designers in hostile environments, for example, a battlefield, where it is impossible to access the sensors and recharge their batteries. Furthermore, when the energy of a sensor reaches a certain threshold, the sensor will become faulty and will not be able to function properly, which will have a major impact on the network performance. Thus, routing protocols designed for sensors should be as energy efficient as possible to extend their lifetime, and hence prolong the network lifetime while guaranteeing good performance overall.

1.4.2 Sensor locations: Another challenge that faces the design of routing protocols is to manage the locations of the sensors. Most of the proposed protocols assume that the sensors either are equipped with global positioning system (GPS) receivers or use some localization technique to learn about their locations.

1.4.3 Limited hardware resources: In addition to limited energy capacity, sensor nodes have also limited processing and storage capacities, and thus can only perform limited computational functionalities. These hardware constraints present many challenges in software development and network protocol design for sensor networks, which must consider not only the energy constraint in sensor nodes, but also the processing and storage capacities of sensor nodes.

1.4.4 Massive and random node deployment: Sensor node deployment in WSNs is application dependent and can be either manual or random which finally affects the performance of the routing protocol. In most applications, sensor nodes can be scattered randomly in an intended area or dropped massively over an inaccessible or hostile region. If the resultant distribution of nodes is not uniform, optimal clustering becomes necessary to allow connectivity and enable energy efficient network operation.

1.4.5 Network characteristics and unreliable environment: A sensor network usually operates in a dynamic and unreliable environment. The topology of a network, which is defined by the sensors and the communication links between the sensors, changes frequently due to sensor addition, deletion, node failures, damages, or energy depletion. Also, the sensor nodes are linked by a wireless medium, which is noisy, error prone, and time varying. Therefore, routing paths should consider network topology dynamics due to limited energy and sensor mobility as well as increasing the size of the network to maintain specific application requirements in terms of coverage and connectivity.

1.4.6 Data Aggregation: Since sensor nodes may generate significant redundant data, similar packets from multiple nodes can be aggregated so that the number of transmissions is reduced. Data aggregation technique has been used to achieve energy efficiency and data transfer optimization in a number of routing protocols.

1.4.7 Diverse sensing application requirements: Sensor networks have a wide range of diverse applications. No network protocol can meet the requirements of all applications. Therefore, the routing protocols should guarantee data delivery and its accuracy so that the sink can gather the required knowledge about the physical phenomenon on time.

1.4.8 Scalability: Routing protocols should be able to scale with the network size. Also, sensors may not necessarily have the same capabilities in terms of energy, processing, sensing, and particularly communication. Hence, communication links between sensors may not be symmetric, that is, a pair of sensors may not be able to have communication in both directions. This should be taken care of in the routing protocols.

Of these, the major issue is the Limited Energy capacity, which ultimately affects the Lifetime of Wireless Sensor networks. Power management refers to the efficient energy use of battery power thereby increasing Network’s Lifetime. The motes are deployed in unattended environments, making it difficult to change batteries. Because of the above reason, the design of Energy aware communication protocols has been prioritized.

The sensor node Lifetime typically exhibits a strong dependency on Battery Life .In many cases Wireless Sensor node has a limited power source (<500mAh, 1.2V), and replenishment of power may be limited or impossible altogether. Power consumption can be allocated to three functional domains: sensing, communication and data processing, each of which requires optimization. In the context of communication, in a multi-hop sensor network a node may play the dual role of collection and processing and of being a data relay point.

Energy efficiency is one of the most important issue in the design of MAC protocol for Wireless Sensor nodes. Several sources contribute to energy inefficiency in MAC layer protocols. The first source of energy waste is collision, which occurs when two or more sensor nodes attempt to transmit simultaneously. The need to retransmit a packet that has been corrupted by a collision increases energy consumption.

The second source of energy waste is idle listening .A sensor node enters this mode when it is listening for a traffic that is not sent. This energy expended monitoring a silent channel can be high in several sensor network applications.

The third source of energy waste is overhearing which occurs when a sensor node receives packets that are destined to other nodes. Due to their low transmitter output, receivers in sensor nodes may dissipate a large amount of power.

The fourth major source of energy waste is caused by control packet overhead. Control packets are required to regulate access to the transmission channel. A high number of control packets transmitted, relative to the number of data packets delivered indicates low energy efficiency.

Finally, frequent switching between different operation modes may result in significant energy consumption. Limiting the number of transitions between sleep and active modes, for example, leads to considerable energy saving.

1.5 ONE REMEDY

1.5.1 POWER MANAGEMENT

Though hardware design construction reduces power consumption, Software power management techniques can greatly decrease the power consumed by RF sensor nodes. TDMA is especially useful for power conservation, since a node can power down or ‘sleep’ between its assigned time slots, waking up in time to receive and transmit messages. The required transmission power increases as the square of the distance between source and destination. Therefore, multiple short message transmission hops require less power than one long hop. In fact, if the distance between source and destination is R, the power required for single-hop transmission is proportional to R2. If nodes between source and destination are taken advantage of to transmit n short hops instead, the power required by each node is proportional to R2/n2. This is a strong argument in favor of distributed networks with multiple nodes, i.e. nets of the mesh variety.

1.6 LITERATURE SURVEY

In order to minimize the energy consumption, some of the previous research works focused on the low energy hardware design of the digital circuits which include micro sensor, low power transceivers etc. But this only reduced the energy consumption up to certain level. However, the energy consumption is mainly due to the communication over the network. Thus the primary focus should be more on design and architecture of the Wireless Sensor Network. For this several research works have proposed energy efficient protocols on Clustering, Routing, Data aggregation etc. These protocols work on the evenly distribution of the energy consumption among the sensor nodes of the Wireless Sensor Network. Routing protocols have a large scope of research work when implemented in a WSN, because the functioning of these protocols depends upon the type of network structure designed for the application or the network operations carried out using these protocols for a specific application model. Routing in wireless sensor networks differs from conventional routing in fixed networks in various ways. There is no infrastructure, wireless links are unreliable, sensor nodes may fail, and routing protocols have to meet strict energy saving requirements. Many routing algorithms were developed for wireless networks in general.

1.6.1 Location-based Protocols

In location-based protocols, sensor nodes are addressed by means of their locations. Location information for sensor nodes is required for sensor networks by most of the routing protocols to calculate the distance between two particular nodes so that energy consumption can be estimated.

1.6.1.1 Geographic Adaptive Fidelity (GAF)[5]: GAF is a location based energy conservation protocol. In GAF redundant nodes are identified based on their geographic locations. The radio of a node is periodically switched off for balancing the load. Location information in GAF is provided by Global Positioning System (GPS) and GAF assumes that the location information is correct. GAF uses the concept of equivalent nodes. Equivalent nodes are intermediate nodes which are same in terms of their connectivity to other nodes with respect to communication. In GAF the network area is divided into small virtual grids such that all nodes in adjacent grids are in each others radio range. Thus in each virtual grid any one of the nodes can be used for routing. In GAF thus energy saving can be done by keeping the radio of one sensor node active per grid and switching off the radios of all the other sensor nodes.

1.6.1.2 Geographic and Energy-Aware Routing (GEAR)[6]: GEAR is an energy-efficient routing protocol proposed for routing queries to target regions in a sensor field, In GEAR, the sensors are supposed to have localization hardware equipped, for example, a GPS unit or a localization system so that they know their current positions. Furthermore, the sensors are aware of their residual energy as well as the locations and residual energy of each of their neighbors. GEAR uses energy aware heuristics that are based on geographical information to select sensors to route a packet toward its destination region. Then, GEAR uses a recursive geographic forwarding algorithm to disseminate the packet inside the target region.

1.6.1.3 Trajectory-Based Forwarding (TBF)[7]: TBF is a routing protocol that requires a sufficiently dense network and the presence of a coordinate system, for example, a GPS, so that the sensors can position themselves and estimate distance to their neighbors. The source specifies the trajectory in a packet, but does not explicitly indicate the path on a hop-by-hop basis. Route maintenance in TBF is unaffected by sensor mobility given that a source route is a trajectory that does not include the names of the forwarding sensors.

1.6.1.4 Bounded Voronoi Greedy Forwarding [BVGF][8]: BVGF uses the concept of Voronoi Diagram in which the sensors should be aware of their geographical positions. In BVGF, a network is modeled by a Voronoi diagram with sites representing the locations of sensors. In this type of greedy geographic routing, a sensor will always forward a packet to the neighbor that has the shortest distance to the destination. It does not help the sensors deplete their battery power uniformly. Each sensor actually has only one next hop to forward its data to the sink. Therefore, any data dissemination path between a source sensor and the sink will always have the same chain of the next hops, which will severely suffer from battery power depletion. BVGF does not consider energy as a metric.

1.6.1.5 Minimum Energy Communication Network (MECN): MECN [9] is a location-based protocol for achieving minimum energy. It is self-reconfiguring protocol that maintains network connectivity in spite of sensor mobility. It computes an optimal spanning tree rooted at the sink, called minimum power topology, which contains only the minimum power paths from each sensor to the sink. It is based on the positions of sensors on the plane and consists of two main phases, namely, enclosure graph construction and cost distribution.

1.6.1.6 Small Minimum-Energy Communication Network (SMECN): SMECN [10] is a routing protocol proposed to improve MECN, in which a minimal graph is characterized with regard to the minimum energy property. This property implies that for any pair of sensors in a graph associated with a network, there is a minimum energy-efficient path between them; that is, a path that has the smallest cost in terms of energy consumption over all possible paths between this pair of sensors. Their characterization of a graph with respect to the minimum energy property is intuitive. In SMECN protocol, every sensor discovers its immediate neighbors by broadcasting a neighbor discovery message using some initial power that is updated incrementally.

1.6.2 Data Centric Protocols

Data-centric protocols differ from traditional address-centric protocols in the manner that the data is sent from source sensors to the sink. In address-centric protocols, each source sensor that has the appropriate data responds by sending its data to the sink independently of all other sensors. However, in data-centric protocols, when the source sensors send their data to the sink, intermediate sensors can perform some form of aggregation on the data originating from multiple source sensors and send the aggregated data toward the sink. This process can result in energy savings because of less transmission required to send the data from the sources to the sink. Some of the data-centric routing protocols for WSNs.

1.6.2.1 Sensor Protocols for Information via Negotiation (SPIN)[11]: SPIN protocol was designed to improve classic flooding protocols and overcome the problems they may cause, for example, implosion and overlap. The SPIN protocols are resource aware and resource adaptive. The sensors running the SPIN protocols are able to compute the energy consumption required to compute, send, and receive data over the network. Thus, they can make informed decisions for efficient use of their own resources. The SPIN protocols are based on two key mechanisms namely negotiation and resource adaptation. SPIN enables the sensors to negotiate with each other before any data dissemination can occur in order to avoid injecting non-useful and redundant information in the network.

1.6.2.2 Directed Diffusion: Directed diffusion [12,13] is a data-centric routing protocol for sensor query dissemination and processing. It meets the main requirements of WSNs such as energy efficiency, scalability, and robustness. Directed diffusion has several key elements namely data naming, interests and gradients, data propagation, and reinforcement. A sensing task can be described by a list of attribute-value pairs. At the beginning of the directed diffusion process, the sink specifies a low data rate for incoming events. After that, the sink can reinforce one particular sensor to send events with a higher data rate by resending the original interest message with a smaller interval. Likewise, if a neighboring sensor receives this interest message and finds that the sender's interest has a higher data rate than before, and this data rate is higher than that of any existing gradient, it will reinforce one or more of its neighbors.

1.6.2.3 Rumor Routing: Rumor routing is a logical compromise between query flooding and event flooding app schemes [14]. Rumor routing is an efficient protocol if the number of queries is between the two intersection points of the curve of rumor routing with those of query flooding and event flooding. Rumor routing is based on the concept of agent, which is a long-lived packet that traverses a network and informs each sensor it encounters about the events that it has learned during its network traverse. An agent will travel the network for a certain number of hops and then die. Each sensor, including the agent, maintains an event list that has event-distance pairs, where every entry in the list contains the event and the actual distance in the number of hops to that event from the currently visited sensor. Therefore, when the agent encounters a sensor on its path, it synchronizes its event list with that of the sensor it has encountered. Also, the sensors that hear the agent update their event lists according to that of the agent in order to maintain the shortest paths to the events that occur in the network.

1.6.2.4 Cougar: The cougar [15] routing protocol is a database approach to tasking sensor networks. The Cougar approach provides a user and application programs with declarative queries of the sensed data generated by the source sensors. These queries are suitable for WSNs in that they abstract the user from knowing the execution plan of its queries. In other words, the user does not know which sensors are contacted, how sensed data are processed to compute the queries, and how final results are sent to the user. The Cougar approach uses a query layer where every sensor is associated with a query proxy that lies between the network layer and application layer of the sensor. This query proxy provides higher level services through queries that can be issued from a gateway node. Furthermore, the Cougar approach employs in-network processing to reduce the total energy consumption and enhance the network lifetime.

1.6.2.5 Active Query Forwarding in Sensor Networks (ACQUIRE): ACQUIRE [16] is another data centric querying mechanism used for querying named data.. It provides superior query optimization to answer specific types of queries, called one-shot complex queries for replicated data. ACQUIRE query (i.e., interest for named data) consists of several sub queries for which several simple responses are provided by several relevant sensors. Each sub-query is answered based on the currently stored data at its relevant sensor. ACQUIRE allows a sensor to inject an active query in a network following either a random or a specified trajectory until the query gets answered by some sensors on the path using a localized update mechanism. Unlike other query techniques, ACQUIRE allows the querier to inject a complex query into the network to be forwarded stepwise through a sequence of sensors.

1.6.2.6 Energy-Aware Data-Centric Routing (EAD): EAD is a novel distributed routing protocol, which builds a virtual backbone composed of active sensors that are responsible for in-network data processing and traffic relaying [17]. In this protocol, a network is represented by a broadcast tree spanning all sensors in the network and rooted at the gateway, in which all leaf nodes’ radios are turned off while all other nodes correspond to active sensors forming the backbone and thus their radios are turned on. Specifically, EAD attempts to construct a broadcast tree that approximates an optimal spanning tree with a minimum number of leaves, thus reducing the size of the backbone formed by active sensors. EAD approach is energy aware and helps extend the network lifetime. The gateway plays the role of a data sink or event sink, whereas each sensor acts as a data source or event source.

1.6.2.7 Gradient-Based Routing: Schurgers et al. [18] have proposed a slightly 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 neighbor is considered the gradient on that link.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.

• 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.

1.6.3 Mobility-based Protocols

Mobility brings new challenges to routing protocols in WSNs. Sink mobility requires energy efficient protocols to guarantee data delivery originated from source sensors toward mobile sinks.

1.6.3.1 Joint Mobility and Routing Protocol: A network with a static sink suffers from a severe problem, called energy sink-hole problem, where the sensors located around the static sink are heavily used for forwarding data to the sink on behalf of other sensors. As a result, those heavily loaded sensors close to the sink deplete their battery power more quickly, thus disconnecting the network. This problem exists even when the static sink is located at its optimum position corresponding to the center of the sensor field [20]. To address this problem, a mobile sink for gathering sensed data from source sensors was suggested [20]. In this case, the sensors surrounding the sink change over time, giving the chance to all sensors in the network to act as data relays to the mobile sink and thus balancing the load of data routing on all the sensors.

1.6.3.2 Scalable Energy-Efficient Asynchronous Dissemination (SEAD): SEAD [21] is self-organizing protocol, which was proposed to trade-off between minimizing the forwarding delay to a mobile sink and energy savings. SEAD considers data dissemination in which a source sensor reports its sensed data to multiple mobile sinks and consists of three main components namely dissemination tree (d-tree) construction, data dissemination, and maintaining linkages to mobile sinks. It assumes that the sensors are aware of their own geographic locations. Every source sensor builds its data dissemination tree rooted at itself and all the dissemination trees for all the source sensors are constructed separately. SEAD can be viewed as an overlay network that sits on top of a location-aware routing protocol, for example, geographical forwarding.

1.6.3.3 Dynamic Proxy Tree-Based Data Dissemination: A dynamic proxy tree-based data dissemination framework [22] was proposed for maintaining a tree connecting a source sensor to multiple sinks that are interested in the source. This helps the source disseminate its data directly to those mobile sinks. In this framework, a network is composed of stationary sensors and several mobile hosts, called sinks. The sensors are used to detect and continuously monitor some mobile targets, while the mobile sinks are used to collect data from specific sensors, called sources, which may detect the target and periodically generate detected data or aggregate detected data from a subset of sensors.

1.6.4 Multipath-based Protocols

Considering data transmission between source sensors and the sink, there are two routing paradigms: single-path routing and multipath routing. In single-path routing, each source sensor sends its data to the sink via the shortest path. In multipath routing, each source sensor finds the first k shortest paths to the sink and divides its load evenly among these paths.

1.6.4.1 Disjoint Paths: Sensor-disjoint multipath routing [23, 25] is a multipath protocol that helps find a small number of alternate paths that have no sensor in common with each other and with the primary path. In sensor-disjoint path routing, the primary path is best available whereas the alternate paths are less desirable as they have longer latency. The disjoint makes those alternate paths independent of the primary path. Thus, if a failure occurs on the primary path, it remains local and does not affect any of those alternate paths. The sink can determine which of its neighbors can provide it with the highest quality data characterized by the lowest loss or lowest delay after the network has been flooded with some low-rate samples. Although disjoint paths are more resilient to sensor failures, they can be potentially longer than the primary path and thus less energy efficient.

1.6.4.2 Braided Paths: Braided multipath [23, 25] is a partially disjoint path from primary one after relaxing the disjointedness constraint. To construct the braided multipath, first primary path is computed. Then, for each node (or sensor) on the primary path, the best path from a source sensor to the sink that does not include that node is computed. Those best alternate paths are not necessarily disjoint from the primary path and are called idealized braided multipath. Moreover, the links of each of the alternate paths lie either on or geographically close to the primary path. Therefore, the energy consumption on the primary and alternate paths seems to be comparable as opposed to the scenario of mutually ternate and primary paths. The braided multipath can also be constructed in a localized manner in which case the sink sends out a primary-path reinforcement to its first preferred neighbor and alternate-path reinforcement to its second preferred neighbor.

1.6.4.3 N-to-1 Multipath Discovery: N-to-1 multipath discovery [26] is based on the simple flooding originated from the sink and is composed of two phases, namely, branch aware flooding (or phase 1) and multipath extension of flooding (or phase 2). Both phases use the same routing messages whose format is given by {mtype, mid, nid, bid, cst, path}, where mtype refers to the type of a message. This multipath discovery protocol generates multiple node-disjoint paths for every sensor. In multihop routing, an active per-hop packet salvaging strategy can be adopted to handle sensor failures and enhance network reliability.

1.6.5 Heterogeneity-based Protocols

In heterogeneity sensor network architecture, there are two types of sensors namely line-powered sensors which have no energy constraint, and the battery-powered sensors having limited lifetime, and hence should use their available energy efficiently by minimizing their potential of data communication and computation. . In this section we discuss uses of heterogeneity in WSNs to extend network lifetime and present a few routing protocols.

1.6.5.1 Cluster-Head Relay Routing (CHR): CHR routing protocol [24] uses two types of sensors to form a heterogeneous network with a single sink: a large number of low-end sensors, denoted by L-sensors, and a small number of powerful high-end sensors, denoted by H-sensors. Both types of sensors are static and aware of their locations using some location service. Moreover, those Land H-sensors are uniformly and randomly distributed in the sensor field. The CHR protocol partitions the heterogeneous network into groups of sensors (or clusters), each being composed of L-sensors and led by an H-sensor. Within a cluster, the L-sensors are in charge of sensing the underlying environment and forwarding data packets originated by other L-sensors toward their cluster head in a multihop fashion. The H-sensors, on the other hand, are responsible for data fusion within their own clusters and forwarding aggregated data packets originated from other cluster heads toward the sink in a multihop fashion using only cluster heads. While L-sensors use short-range data transmission to their neighboring H-sensors within the same cluster, H-sensors perform long-range data communication to other neighboring H-sensors and the sink.

1.6.6 QoS-based Protocols

In addition to minimizing energy consumption, it is also important to consider quality of service (QoS) requirements in terms of delay, reliability, and fault tolerance in routing in WSNs. In this section, we review a sample QoS based routing protocols that help find a balance between energy consumption and QoS requirements.

1.6.6.1 Sequential Assignment Routing (SAR): SAR [25] is one of the first routing protocols for WSNs that introduces the notion of QoS in the routing decisions. It is a table-driven multi-path approach striving to achieve energy efficiency and fault tolerance. Routing decision in SAR is dependent on three factors: energy resources, QoS on each path, and the priority level of each packet. The SAR protocol creates trees rooted at one-hop neighbors of the sink by taking QoS metric, energy resource on each path and priority level of each packet into consideration. By using created trees, multiple paths from sink to sensors are formed. One of these paths is selected according to the energy resources and QoS on the path. Failure recovery is done by enforcing routing table consistency between upstream and downstream nodes on each path. Failure recovery is done by enforcing routing table consistency between upstream and downstream nodes on each path. Simulation results showed that SAR offers less power consumption than the minimum-energy metric algorithm, which focuses only the energy consumption of each packet without considering its priority. Although, this ensures fault-tolerance and easy recovery, the protocol suffers from the overhead of maintaining the tables and states at each sensor node especially when the number of nodes is huge.

1.6.6.2 Speed: SPEED [26] is another QoS routing protocol for sensor networks that provides soft real time end-to-end guarantees. The protocol requires each node to maintain information about its neighbors and uses geographic forwarding to find the paths. In addition, SPEED strive to ensure a certain speed for each packet in the network so that each application can estimate the end-to-end delay for the packets by dividing the distance to the sink by the speed of the packet before making the admission decision. Moreover, SPEED can provide congestion avoidance when the network is congested. The routing module in SPEED is called Stateless Geographic Non-Deterministic forwarding (SNFG) and works with four other modules at the network layer. The beacon exchange mechanism collects information about the nodes and their location. SPEED does not consider any further energy metric in its routing protocol. Therefore, for more realistic understanding of SPEED’s energy consumption, there is a need for comparing it to a routing protocol, which is energy-aware.

1.6.6.3 Energy-Aware QoS Routing Protocol: In this QoS aware protocol [27] for sensor networks, real time traffic is generated by imaging sensors. The proposed protocol extends the routing approach in and finds a least cost and energy efficient path that meets certain end-to-end delay during the connection. The link cost used is a function that captures the nodes’ energy reserve, transmission energy, error rate and other communication parameters. In order to support both best effort and real-time traffic at the same time, a class-based queuing model is employed. The queuing model allows service sharing for real-time and non-real-time traffic. The protocol finds a list of least cost paths by using an extended version of Dijkstra’s algorithm and picks a path from that list which meets the end-to-end delay requirement. Simulation results show that the proposed protocol consistently performs well with respect to QoS and energy metrics, however, it does not provide flexible adjusting of bandwidth sharing for different links.

1.6.7 Hierarchical Routing

As shown in figure(2.1), a hierarchical approach breaks the network into clustered layers. Nodes are grouped into clusters with a cluster head that has the responsibility of routing from the cluster to the other cluster heads or base stations. Data travel from a lower clustered layer to a higher one. Although, it hops from one node to another, but as it hops from one layer to another it covers larger distances. This moves the data faster to the base station. In the cluster-based hierarchical model, data is first aggregated in the cluster then sent to a higher-level cluster head. As it moves from a lower level to a higher one, it travels greater distances, thus reducing the travel time and latency. This model is better than the one hop or multi-hop model.

Figure 1.1: Cluster-based Hierarchical Model

Further, in cluster-based model only cluster-heads performs data aggregation whereas in the multi-hop model every intermediate node performs data aggregation. As a result, the cluster-based model is more suitable for time critical applications than the multi-hop model. However, it has one drawback, namely, as the distance between clustering level increases, the energy spent is proportional to the square of the distance. This increases energy expenditure. Despite this drawback, the benefits of this model are more important than drawback. A cluster based hierarchical model offers a better approach to routing for WSNs.

Power-Efficient Hierarchical Cluster Routing Protocols

Clustering algorithms for traditional wireless ad hoc networks are not well suited for WSNs. Some of the special features of WSNs are as follows:

Sensor nodes are densely deployed.

Sensor nodes are prone to failure.

The large number of sensors nodes in a WSN and are limited in power, computational capacities, and storage memory.

The topology of a WSN may change rather frequently because a sensor node may alternate between the active and sleep states.

Sensor nodes may not have global identification (ID) because of the large amount of overhead and the large number of sensors.

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. This means that 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 and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the BS. Hierarchical routing is mainly two-layer routing where one layer is used to select cluster heads and the other layer is used for routing. A variety of protocols have been proposed for prolonging the life of WSN and for routing the correct data to the base station. Some of the hierarchical protocols are LEACH, PEGASIS, HPIGASIS, TEEN, APTEEN, HEED and EARP.

1.6.7.1 Low Energy Adaptive Clustering Hierarchy protocol (LEACH) [28,29]: Low-energy adaptive clustering hierarchy (LEACH): LEACH [28,29] is the first and most popular energy-efficient hierarchical clustering algorithm for WSNs that was proposed for reducing power consumption. In LEACH, the clustering task is rotated among the nodes, based on duration. Direct communication is used by each cluster head (CH) to forward the data to the base station (BS). It uses clusters to prolong the life of the wireless sensor network. LEACH is based on an aggregation (or fusion) technique that combines or aggregates the original data into a smaller size of data that carry only meaningful information to all individual sensors. LEACH divides the a network into several cluster of sensors, which are constructed by using localized coordination and control not only to reduce the amount of data that are transmitted to the sink, but also to make routing and data dissemination more scalable and robust. LEACH uses a randomize rotation of high-energy CH position rather than selecting in static manner, to give a chance to all sensors to act as CHs and avoid the battery depletion of an individual sensor and dieing quickly.

1.6.7.2 Power-Efficient Gathering in Sensor Information Systems (PEGASIS): PEGASIS [30] is an extension of the LEACH protocol, which forms chains from sensor nodes so that each node transmits and receives from a neighbor and only one node is selected from that chain to transmit to the base station (sink). The chain construction is performed in a greedy way. Unlike LEACH, PEGASIS avoids cluster formation and uses only one node in a chain to transmit to the BS (sink) instead of using multiple nodes In PEGASIS routing protocol, the construction phase assumes that all the sensors have global knowledge about the network, particularly, the positions of the sensors, and use a greedy approach. When a sensor fails or dies due to low battery power, the chain is constructed using the same greedy approach by bypassing the failed sensor. In each round, a randomly chosen sensor node from the chain will transmit the aggregated data to the BS, thus reducing the per round energy expenditure compared to LEACH. Simulation results showed that PEGASIS is able to increase the lifetime of the network twice as much the lifetime of the network under the LEACH protocol. 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.

1.6.7.3 Hybrid, Energy-Efficient Distributed Clustering (HEED): HEED [31] extends the basic scheme of LEACH by using residual energy and node degree or density as a metric for cluster selection to achieve power balancing. It operates in multi-hop networks, using an adaptive transmission power in the inter-clustering communication. HEED was proposed with four primary goals namely (i) prolonging network lifetime by distributing energy consumption, (ii) terminating the clustering process within a constant number of iterations, (iii) minimizing control overhead, and (iv)producing well-distributed CHs and compact clusters. In HEED, the proposed algorithm periodically selects CHs according to a combination of two clustering parameters. The primary parameter is their residual energy of each sensor node (used in calculating probability of becoming a CH) and the secondary parameter is the intra-cluster communication cost as a function of cluster density or node degree (i.e. number of neighbors). The cluster selection deals with only a subset of parameters, which can possibly impose constraints on the system. These methods are suitable for prolonging the network lifetime rather than for the entire needs of WSN.

1.6.7.4 Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN): TEEN [32] is a hierarchical clustering protocol, which groups sensors into clusters with each led by a CH. The sensors within a cluster report their sensed data to their CH. The CH sends aggregated data to higher level CH until the data reaches the sink. Thus, the sensor network architecture in TEEN is based on a hierarchical grouping where closer nodes form clusters and this process goes on the second level until the BS (sink) is reached. TEEN is useful for applications where the users can control a trade-off between energy efficiency, data accuracy, and response time dynamically. TEEN uses a data-centric method with hierarchical approach. Important features of TEEN include its suitability for time critical sensing applications. .However, TEEN is not suitable for sensing applications where periodic reports are needed since the user may not get any data at all if the thresholds are not reached.

1.6.7.5 Adaptive Periodic Threshold Sensitive Energy Efficient Sensor Network Protocol (APTEEN): APTEEN [33] is an improvement to TEEN to overcome its shortcomings and aims at both capturing periodic data collections (LEACH) and reacting to time-critical events (TEEN). Thus, APTEEN is a hybrid clustering-based routing protocol that allows the sensor to send their sensed data periodically and react to any sudden change in the value of the sensed attribute by reporting the corresponding values to their CHs. The architecture of APTEEN is same as in TEEN, which uses the concept hierarchical clustering for energy efficient communication between source sensors and the sink. APTEEN supports three different query types namely (i) historical query, to analyze past data values, (ii) one-time query, to take a snapshot view of the network; and (iii) persistent queries, to monitor an event for a period of time. APTEEN guarantees lower energy dissipation and a larger number of sensors alive.

1.6.7.6 Virtual Grid Architecture (VGA): It is an energy-efficient routing paradigm proposed in[37]. The protocol utilizes data aggregation and in-network processing to maximize the network lifetime. Due to the node stationary and extremely low mobility in many applications in WSNs, a reasonable approach is to arrange nodes in a fixed topology. A GPS-free approach is used to build clusters that are fixed, equal, adjacent, and non-overlapping with symmetric shapes. In [37], square clusters were used to obtain a fixed rectilinear virtual topology. Inside each zone, a node is optimally selected to act as CH. Data aggregation is performed at two levels: local and then global. The set of CHs, also called Local Aggregators (LAs), perform local aggregation, while a subset of these LAs are used to perform global aggregation. However, the determination of an optimal selection of global aggregation points, called Master Aggregators (MAs), is NP-hard.

1.6.7.7 Self-organizing protocol (SOP)[36]: The architecture supports heterogeneous sensors that can be mobile or stationary. Some sensors, which can be either stationary or mobile, probe the environment and forward the data to designated set of nodes that act as routers. Router nodes are stationary and form the backbone for communication. Collected data are forwarded through the routers to more powerful sink nodes. Each sensing node should be reachable to a router node in order to be part of the network. A routing architecture that requires addressing of each sensor node has been proposed. Sensing nodes are identifiable through the address of the router node it is connected to. The routing architecture is hierarchical where groups of nodes are formed and merge when needed. In order to support fault tolerance, Local Markov Loops (LML) algorithm, which performs a random walk on spanning trees of a graph, is used in broadcasting. The algorithm for self organizing.

1.6.7.8 Energy Efficient Homogenous Clustering Algorithm for Wireless Sensor Networks: Singh et al.[4] proposed homogeneous clustering algorithm for wireless sensor network that saves power and prolongs network life. The life span of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. A new cluster head is selected on the basis of the residual energy of existing cluster heads, holdback value, and nearest hop distance of the node. The homogeneous algorithm makes sure that every node is either a cluster head or a member of one of the clusters in the wireless sensor network. In the proposed clustering algorithm the cluster members are uniformly distributed, and thus, the life of the network is more extended. Further, in the proposed protocol, only cluster heads broadcast cluster formation message and not the every node. Hence, it prolongs the life of the sensor networks. The emphasis of this approach is to increase the life span of the network by ensuring a homogeneous distribution of nodes in the clusters so that there is not too much receiving and transmitting overhead on a Cluster Head.

Table 1.1: Classification and Comparison of routing protocols in WSNs.

Routing Protocols

Classification

Power Usage

Data Aggre

Scalability

Query based

Overhead

Data Delivery Model

QoS

SPIN

Flat/DC

Ltd

Yes

Ltd

Yes

Low

Event driven

No

DD

Flat/DC

Ltd

Yes

Ltd

Yes

Low

Demand driven

No

RR

Flat

Low

Yes

Good

Yes

Low

Demand driven

No

GBR

Flat

Low

Yes

Ltd

Yes

Low

Hybrid

No

CADR

Flat

Ltd

Yes

Ltd

Yes

Low

Continuously

No

COUGAR

Flat

Ltd

Yes

Ltd

Yes

High

Query driven

No

ACQUIRE

Flat/DC

Low

Yes

Ltd

Yes

Low

Complex query

No

LEACH

Hier/NC

High

Yes

Good

No

High

Cluster head

No

TEEN&

APTEEN

Hier

High

Yes

Good

No

High

Active threshold

No

PEGASIS

Hier

Max

No

Good

No

Low

Chains based

No

VGA

Hier

Low

Yes

Good

No

High

Good

No

SOP

Hier

Low

No

Good

No

High

Continuously

No

GAF

Hier/Loc

Ltd

No

Good

No

Mod

Virtual grid

No

SPAN

Hier/loc

Ltd

Yes

Ltd

No

High

Continuously

No

GEAR

Loc

Ltd

No

Ltd

No

Mod

Demand driven

No

SAR

DC

High

Yes

Ltd

Yes

High

Continuously

Yes

SPEED

Loc/DC

Low

No

Ltd

Yes

Less

geographical

Yes

*Hier - Hierarchical

*DC - Data centric

*Loc - Location

*NC - Node-centric