A Review of Power Optimization Strategies in Wireless Sensor Network

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 3, Issue 10, October 2013)

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A Review of Power Optimization Strategies in Wireless Sensor

Network

Santosh Ahirwar

1

, Pushpraj Tanwar

2

1,2

Electronics & Communication Engineering Department, Radharaman Institute of Technology & Science, Bhopal, India

Abstract— The challenging task of wireless sensor network is to increase the lifetime as they are equipped with critical battery power. Once WSN is deployed in disaster areas, inaccessible terrains or polluted environments, battery recharge or replacement is impossible. For optimizing the battery power of the sensor network, various energy efficient routing strategies are applied. This paper reviews the recent energy optimizing routing protocols and their performance. We first outline from basic sensor network model to routing strategy in terms of energy optimization. Our review concludes with the recommendations to the future scope in the energy optimization model for the wireless sensor networks.

Keywords—Wireless Sensor Networks, Clustering, Cluster Head, Base Station, Low Energy, Energy Efficient Routing.

I. INTRODUCTION

Modern advanced technologies in microelectronic mechanical systems (MEMS) [2][3] and wireless communication technologies have developed small sized, low- cost, low-power, and multifunctional smart sensor nodes in a wireless sensor network (WSN). Wireless sensor nodes are deployed and networked through internet and wireless links, which works for various industrial, scientific and military applications, for example, environmental monitoring, battle field surveillance, and industry process control, healthcare applications, traffic control and home automation. Distributed electromechanical sensing devices (sensor nodes) work together to monitor physical or environmental conditions such as temperature, humidity, motion, sound, radiation, vibration and pressure.

The modern networks are bi-directional, also

enabling control of sensor activity. Unlike traditional wireless communication networks such as cellular systems and mobile ad hoc networks (MANET), WSNs [1] have unique characteristics such as heterogeneity of nodes, ability to withstand harsh environmental conditions, denser level of node deployment, severe energy, computation, and storage constraints, which present much new advancement in the development and application of WSNs. The WSN [1] is built up of "nodes" – from a few to several hundreds or thousands, where each node is connected to one (or sometimes several) sensors.

Each sensor network node has many components: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors (electromechanical or electronic) and an one time energy source (battery). The cost of sensor nodes is variable, depending on the functionality, applications and complexity of the individual sensor nodes. Size and cost of sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs [1] can vary from a simple star network to an advanced multi-hop wireless mesh network. The signal propagation technique between the hops of the network can be routing or flooding.

In this paper we present a survey of energy optimizing protocols and scheme used in wireless sensor networks. Our aim is to provide a better understanding of the current issues in this emerging field for energy conservation.

II. ARCHITECTURE OF WSN

The hundreds or thousands number of sensor nodes form a sensor network to produce high-quality information about their environment [4]. The functionalities like sensing, processing, storing, data packet transmission, location finding, power consumption etc. are available in each of the nodes.

The major components of WSN [4] are:

Sensor Node: It is the main component of a WSN. Sensor nodes take multiple functions in a network, like as simple sensing, processing, data storing, routing, path searching and data transmission.

Clusters: Normally sensor nodes are grouped into clusters. Clusters are the organizational unit for WSNs. The dense nature of these networks requires them to be broken down into clusters and task is simplified for a communication.

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Base Station: The base station is at the upper level of the hierarchical WSN. It provides the communication link between the sensor network and the end-user. Base station receives data from cluster heads.

End User: The sensed data in a wireless sensor network can be used for various applications. Therefore, a particular application may make use of the network data over the internet, using a Laptop, PDA, a desktop computer. In a queried sensor network (where the required data is gathered from a query sent through the network). This query is generated by the end user.

[image:2.612.52.302.283.508.2]

Figure 1: Architecture of Wireless Sensor Network [4]

The architectural view of sensor network [4] is shown in figure 1. Sensing unit, processing unit, transmission unit, and power unit are the four major components of sensor nodes assigned with dissimilar jobs. Sensing unit is used to trace the physical environment and tells the CPU to compute or process and store the data it sensed. Transmission unit is tasked to receive the information from CPU and transmit it to the cluster head or base station. Power unit regulate battery power to sensor node.

There are different strategies to achieve better lifetime which include energy efficient routing. Routing in wireless sensor networks is very challenging task due to several characteristics that distinguish these networks from other wireless networks like mobile ad hoc networks (MANET) or cellular networks. These include dense deployment of sensor nodes, significant data redundancy, limited

In section III, the energy efficient routing protocols are discussed which helps in raising the energy optimization of the node.

III. REVIEW OF ENERGY OPTIMIZING PROTOCOLS

In this section, we are reviewing energy optimizing protocols [1] based on their classifications. The sensor nodes are constrained to limited one time battery power resources itself, so the main purpose is how to design an effective and energy optimizing protocol in order to enhance the networks lifetime for specific application environment.

[image:2.612.320.567.369.534.2]

Routing protocols [1] are classified into four categories as shown in Table 1: Data Centric Protocols, Hierarchical Based Routing Protocol (Clustering), Location-Based Routing Protocol (Geographic) and Network Flow & QoS Aware Protocol depending on the network structure in WSNs.

Table 1

Categories of Routing Protocols [1]

Category Representative Protocols Data Centric

Protocols

Flooding and Gossiping, SPIN, Directed Diffusion, Rumor Routing, Gradient Based Routing, Energy-Aware Routing, CADR, COUGAR & ACQUIRE.

Hierarchical Protocols

LEACH, PEGASIS, H-PEGASIS, TEEN & APTEEN.

Location Based Protocol

MECN & SMECN, GAF & GEAR.

Network Flow & QoS Aware Protocol

Maximum Lifetime Energy Routing, Maximum Lifetime Data Gathering, Minimum Cost Forwarding, SAR & SPEED.

(a). Data Centric (Flat Based Routing) Protocols:

In data centric routing [5], all nodes are usually equal and have the same functions. It is very difficult to assign global identification to each node in wireless sensor networks because the deployment of sensor network is very dense and dynamic in the. In data-centric routing [5], base station sends queries to certain areas and waits for data from sensors located in the selected areas. An attribute-based naming scheme is used to specify the properties of data to facilitate data-centric characteristics of sensor queries.

(i). Flooding and Gossiping:

Flooding and gossiping [6] are the mostly traditional network routing. In flooding routing, each sensor node acts Internet

BS

Radio Transceiver Processor

Storage Storage Sensor ADC

Mobilizer Position Finding System

Power Unit (Battery)

Sensing Unit Processing Unit Transmission Unit

User

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214 Each sensor node sends every data packet to every one of its neighbors except the source node. When the packet arrives at the destination node or the maximum number of hops is reached, the packet sending process is stopped. Although flooding is easier, it has many disadvantages like implosion (duplicate data packet sent to same node), overlap (two nodes sensing the same area send same packets to the same neighbor) and resource blindness problem (consumes huge amount of energy without consideration for the energy constraints).

Gossiping [6] eliminates the problem of implosion by sending information to a random neighbor instead of traditional broadcasting routing method of sending data packets to all neighbors.

(ii). SPIN (Sensor Protocols for Information via Negotiation):

SPIN [7] is based on negotiation between the nodes by means of data advertisement through meta-data (a high-level name to their data). SPIN [7] performs meta-data negotiations before any data packet is transmitted. SPIN avoids typical flooding problems like overlaps, implosions and resource-blindness.

The disadvantage of SPIN [7] is that it uncertain whether the data packet will certainly reach to the target or not and it is inefficient for high-density distribution of sensor nodes. So, SPIN [7] is not better option for applications.

(iii). Directed Diffusion:

Directed diffusion [8] is data-centric; all the sensor nodes in a directed diffusion-based network are application-oriented. This protocol achieves energy savings by selecting better routing paths and by data aggregation (caching and processing data) in the network. SPIN protocol allow sensors nodes to send advertisement packets for the availability of data and the sensor nodes which are interested [7], sends query for that data packet. But in Directed Diffusion the base station sends queries to the sensor nodes if a specific data packet is available or not.

The main advantages of directed diffusion [8] are:

1) It is data centric, so all data packet transmission is

neighbor-to-neighbor without a node identification addressing method. Each sensor node can do caching and aggregation for sensing. Caching is a big advantage for energy efficiency and delay.

2) Since it is on demand and there is no global network

topology mechanism so Direct Diffusion [8] is highly energy efficient.

Directed Diffusion [8] is not a better choice for the application like environmental monitoring since it requires continuous data delivery to the base station that will not work efficiently with a query-driven on-demand data model.

(iv). Energy-Aware Routing:

Energy aware routing [9] protocol is power efficient strategy to minimize the energy cost for data packet transmission and can enhance the network lifetime. Unlike directed diffusion [8], data packet transmission is done via several low cost optimal routing paths at higher rates instead of transmitting through one optimal path. The transmission routing path is selected by choosing a probability value of each routing path. The probability values balance the initial network load and increase the network lifetime.

The disadvantage of energy-aware routing [9] is that it needs local information exchange among neighbor sensor nodes and all sensor nodes have a unique address, which enlarges the cost of routing paths.

(v). Gradient-Based Routing:

Gradient-Based routing [10] is an improvement of Directed Diffusion, in order to get the total minimum number of hop rather than the total shortest time. In the traditional gradient protocol, hop count is the only metric, which measures the quality of route. The new proposed, gradient routing protocol [10] which is considered as the hop count as well as the remaining energy of each sensor node, while relaying data from source node to the base station. This protocol [10] is applicable in handling the frequently change of the topology of the sensor network due to failure of sensor node.

(b) Hierarchical-Based Routing (Clustering):

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(i). LEACH:

LEACH (Low-Energy Adaptive Clustering Hierarchy) [12] is the first hierarchical-based routing protocol. When the sensor node in the WSN fails or its battery power goes down then LEACH [12] protocol is used in the network. In LEACH, [12] wireless sensor nodes are grouped into local clusters and cluster members elect their cluster head (CH) to avoid extra energy consumption by sensor nodes and incorporate data aggregation which reduces the amount of data packet sent to the base station, to enhance the life time of the network. Therefore this protocol has an effect upon energy saving.

Two-Level Hierarchy LEACH (TL-LEACH) is an improved form of the LEACH protocol which has of two levels of cluster heads (primary and secondary) in place of a single cluster head. The advantage of two-level structure of TL-LEACH is that it minimizes the amount of sensor nodes that transmit data packets to the base station, effectively reducing the total energy consumption.

(ii). PEGASIS and Hierarchical-PEGASIS:

PEGASIS [13] (Power-Efficient Gathering in Sensor Information Systems) is chain-based routing protocol that is an improved modification of LEACH. PEAGSIS [13] designs a node chain when sensor nodes are deployed randomly in the environment then each sensor node communicates only with a nearer neighbor, take its turns and transmit data packet to the base station, so it reduces the amount of energy consumed per round.

PEGASIS [13] performs better than LEACH [12] by elimination of taking dynamic cluster formation, since data packet transmission is asynchronous, the transmission time will be too long. Hierarchical-PEGASIS makes a further improvement; it allows concurrent data packet transmission when the nodes are not adjacent.

As compared to LEACH [12], the two protocols eliminate the overhead of forming cluster, but both of them do not consider the energy condition of next hop into consideration when choosing a routing path, so they are not applicable for heavy-loaded network. For large amount of sensor nodes in WSNs, the delay of data packet transmission is larger, so they are not well and not suitable for sensor networks where global identification is not easy to obtain.

(iii). TEEN and APTEEN:

TEEN [14] (Threshold Sensitive Energy Efficient Sensor Network) protocol and it was first developed for reactive networks. It is mostly used in temperature sensing application.

TEEN [14] is based on hierarchical clustering which divide the sensor nodes twice for clustering group in for detecting the sudden changes in the sensed data such as temperature. After the clusters formation, TEEN [14] divides the cluster head (CH) into the second-level cluster head and uses Hard and Soft threshold values to detect the sudden changes.

Hard threshold reduces the number of packet transmissions by allowing the sensor nodes to transmit packet only when the sensed value is in the high range of consideration. The soft threshold reduces the number of packet transmissions by preventing all the packet transmissions which occurs when there is minimum change in the sensed value.

The disadvantage of TEEN [14] is that it is not applicable for applications where regular basis data is needed. The practical implementation is not certain that there are no collisions in the cluster. TDMA scheduling of the sensor nodes can be applied for this problem but it creates a delay in the reporting of the time-critical data. CDMA may be possible solution to this problem. TEEN [14] is best applicable for time critical applications such as intrusion detection, explosion detection, radiation detection etc.

The Adaptive Threshold Sensitive Energy Efficient Sensor Network protocol (APTEEN) is an improvement of TEEN and targeted at both capturing periodic sensed data collections and reacting to time critical events. The architecture of APTEEN is same as in TEEN. In APTEEN, once the cluster heads are selected, in each cluster period, the cluster head broadcasts the parameter such as sensed values, threshold and count time to all its cluster nodes.

The performance of APTEEN lies between TEEN and LEACH in terms of battery power consumption and lifetime of the sensor network. While sensing the environment, TEEN protocol only transmits the time critical sensing data, whereas APTEEN supports periodic report for time-critical events. The disadvantages of the two protocols are the overhead and complexity of forming clusters.

(c). Location-Based Routing:

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216 (i). MECN and SMECN:

Minimum Energy Communication Network (MECN) [15] establishes a minimum energy network for wireless sensor networks by utilizing low power GPS. This protocol assumes network as a mobile network, is best applicable to sensor networks, which are not mobile. MECN assumes a master site region as the information base station, which is always the case for the wireless sensor networks.

MECN [15] identifies a relay region for every sensor node. The relay region consists of sensor nodes in a surrounding area where transmission of data packets via those nodes is more energy efficient than direct data packet transmission.

MECN [15] is robust, dynamic and self-reconfiguring, thus can dynamically deploy to sensor nodes failure or the deployment of new sensors.

The small minimum energy communication network (SMECN) [15] is an enhancement of MECN. In MECN, it is assumed that every sensor node can transmit data packets to other nodes, which is not practicable every time. The sub-network formed by SMECN for minimum energy relaying is probably smaller (in terms of number of edges) than the one constructed in MECN if broadcasts are able to reach to all nodes in a circular area around the sender node. SMECN [15] uses less power consumption than MECN and less maintenance cost of the links. However, finding a sub-network with smaller number of edges introduces more overhead in this protocol.

(ii). GEAR:

GEAR [16] uses energy aware and geographically informed neighbor selection mechanism to route a packet towards the target sensor nodes or base station. GEAR [16] helps in balancing power consumption and enhances the network lifetime. When a closer neighbor node to the destination node exists, GEAR [16] forward the data packet to the destination by selecting a next-hop among all neighboring nodes that are nearer to the destination. When all neighbor nodes are at larger distance, then there is a ‗hole‘ problem, GEAR [16] forward the data packet by selecting a next-hop node that minimizes some cost value of this neighbor node. Recursive Geographic Forwarding protocol is used to disseminate the packet within the area.

GEAR [16] is compared with similar non-energy aware routing protocol GPSR, which is one of the earlier works in geographic routing protocol that uses planar graphs to solve the problem of holes. GEAR delivers 70% to 80% more data packets than GPSR. For uniform traffic pairs, GEAR delivers 25 - 35% more data packets than GPSR.

(iii). GAF and HGAF

In GAF [17] (Geographical Adaptive Fidelity) Protocol, large numbers of sensor nodes are deployed in observed region and only few nodes in the observed region are selected to transmit data packets, while the other sensor nodes do not work. In this mechanism, GAF [17] reduces the number of sensor nodes required to form a sensor network and enhances the lifetime of the sensor node.

Hierarchical Geographical Adaptive Fidelity (HGAF) [18] protocol saves much more battery power by enlarging the cell of GAF by adding a layered structure for selecting an active sensor node in each cell. GAF [17] improves battery power by enlarging the size of the previous cell.

HGAF [18] has limitation to the position of active sensor node in a cell and synchronizes the position in each cell among all other cells. By this improvement, the connectivity between active sensor nodes in two adjacent cells can be certainly strong for a larger cell than in GAF.

HGAF [18] performs better than GAF in terms of survived sensor nodes and the data packet delivery ratio when the sensor node density is high in WSN. The lifetime of dense and randomly distributed networks with HGAF is very longer than GAF.

(d). Network-flow-based routing / Quality-of-Service based routing:

Main focus of Network-flow-based routing protocols is to optimize the balancing of network traffic and to enhance the network lifetime. Maximum lifetime energy routing for instance defines link costs depending on remaining energy and required transmission energy, which are utilized to even out the energy expenditures of the nodes. Quality-of-Service (QoS) functions like end-to-end ensures and further examination of correct data packet transmission are usually an advanced feature of routing protocols. An example for a Quality-of-Service approach is the location-based protocol SPEED, which allows the estimation of end-to-end delays by ensuring a higher data packet speed.

IV. POWER CONSUMPTION IN WSN

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Malfunctioning of a few nodes and can cause significant topological change and might require re-routing of packets and reorganization of the network. Hence, power conservation and power management takes an additional importance. It is for these reasons that researchers are currently focusing on the design of power aware protocols and algorithms for sensor network.

The main operations in the sensor node by which the battery power is consumed are:

1. Sensing 2. Processing 3. Storing 4.Transmitting

The transmitting of data consumes more energy as compared to other functions.

V. PERFORMANCE MEASURES

We define here the measures that can be used to evaluate the performance of routing protocol of WSNs.

Network lifetime: This is the time interval from the start of operation of the wireless sensor network until the battery discharge of the first alive sensor node.

Number of cluster heads per round: This is an instantaneous measure for the number of sensor nodes which would send data packet directly to the base station aggregated from their cluster members.

Number of alive (total, super, advanced and normal) nodes per round:This measures the total number of sensor nodes and that of each type that has not yet expended all of their energy.

Throughput:It is the total data rate sent over the wireless sensor network, the data rate sent from cluster heads to the base station as well as the data rate sent from the nodes to their cluster heads.

VI. CONCLUSION &FUTURE RESEARCH

In this review, we presented the comprehensive review and theoretical study of different strategies by which network lifetime of the wireless sensor networks can be enhanced. Routing is the most important strategy that gives energy efficiency, and improves the network lifetime. Routing protocols are based on four categories: Data centric routing, Hierarchical-based routing, Location-based and Network Flow & QoS Aware based routing on the basis of network structure. Many issues and challenging tasks such as effectiveness, scalability, adaptability etc. still exist that need to be solved in the sensor networks. Although many of these routing strategies look effective, there are still many challenging tasks that need to be improved in the sensor networks. We highlighted those challenging tasks and highlighted future research directions

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