Saliva Based Diagnostic Sensor

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Realistic Approach towards Quantitative Analysis and Simulation of EEHC-Based Routing for Wireless Sensor

Abstract

This paper presents the realistic approach towards the quantitative analysis and simulation of Energy Efficient Hierarchical Cluster (EEHC)-based routing for wireless sensor networks. Here the efforts have been done to combine analytical hardware model with the modified EEHC-based routing model. The dependence of various performance metrics like: optimum number of clusters, Energy Consumption, and Energy consumed per round etc.

based on analytical hardware sensor model and EEHC model has been presented. Keywords: EEHC, LEACH, Wireless Sensor Networks, Energy Consumption.

The Basic Element Of A Wireless Sensor Networks

(WSN) is the sensor node, where it is consisted of three main functional components that separately deliver sensory, communication and processing capabilities.

Because Of The Difficulty And Cost Of Sensor Node

replacement in face of battery drainage or system failure, hardware reliability is also another major concern in WSN design. Therefore keeping in realistic facts and operational conditions the focus of this paper is report the realistic quantitative analysis EEHC-based routing for WSN.

Many energy-efficient routing protocols are designed

Based On The Clustering Structure. The Randomized

clustering algorithm to organize sensors into clusters in a WSN was proposed Bandyopadhyay and Coyle . Further the computation of the optimal probability of becoming a cluster head was presented. In , Moscibroda and Wattenhofer defined the maximum cluster-lifetime problem, and they proposed distributed, randomized algorithms that approximate the optimal solution to maximize the lifetime of dominating sets on WSNs.

Low-Energy Adaptive Clustering Hierarchy (Leach)

is one of the first hierarchal routing approaches for WSNs. It is a well-known clustering protocol for wireless sensor networks and most of the clustering algorithms are based on this algorithm. This protocol uses only two layers for communication. LEACH includes distributed cluster

Global

communication, and randomized rotation of cluster-heads. In literature it has been reported that LEACH performs over a factor of 7 reductions in energy dissipation compared to flat base routing algorithm such as direct diffusion . But the main problem with LEACH protocol lies in the random selection of cluster heads. There exists a probability that the cluster heads formed are unbalanced and may remain in one part of the network making some part of the network unreachable.

As An Extension Of Leach [3, 5-7], The Proposed

protocol introduces a head set for the control and management of clusters. Although S-MAC divides the network into virtual clusters, the proposed protocol divides the network into a few real clusters that are managed by a virtual cluster-head. The results reported in this paper are the efforts towards the realistic approach by extending of work reported in [7, 9]. The derivations for transceiver power consumption modeled in and are considered to report modified quantitative analysis for the EEHC based routing for WSN.

The rest of this paper is organized as follows. After mentioning the introduction in section 1, the related work is discussed in section 2. In Section 3, we have described the quantitative analysis for EEHC-based WSN. In section 4 the performances have been evaluated of EEHC via simulations. Finally, Section 5 concludes the paper and future work is pointed out.

2. Related Work

The cluster-based structure is widely acknowledged as an energy conserving way to facilitate the network self-

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optimization of a WSN , in particular for periodic reporting applications, where data from many sources converge into one data sink. Clustering based routing performs very efficiently when applied to homogeneous WSNs.

Mhatre And Rosenberg Studied The Case Of Multi-

hop routing within each cluster, which is called M-

Leach. In M-Leach, Only Powerful Nodes Can Become

the cluster-heads. Energy Efficient Cluster Scheme (EECS) elects the cluster-heads with more residual energy through local radio communication. But on the other hand, it increases the requirement of global knowledge about the distances between the cluster-heads and the base station. A new adaptive strategy is proposed

Known As Leach-B In To Choose Cluster-Heads And

to vary their election frequency according to the dissipated energy. However, the simulation results divulge that there is some degree of improvement using LEACH-B.

Moreover, an improved scheme of LEACH was proposed,

Named Leach-C . In Leach-C, A Centralized

algorithm at the base station makes cluster formation. However, LEACH-C is not feasible for larger networks because nodes far away from the base station will have problem sending their states to the base station and as the role of cluster heads rotates so every time the far nodes will not reach the base station in quick time increasing the latency and delay.

Further, The Clustering Protocol Known As Leach-E

was proposed by Heinzelman et.al. in . In this protocol it is proposed to elect the cluster-heads according to the energy left in each node. The drawback of LEACH-E is that it requires the assistance of routing protocol, which should allow each node to know the total energy of

Network. Stable Election Protocol (Sep) Was

developed for the two-level heterogeneous networks. SEP performs poorly in multi-level heterogeneous networks and when heterogeneity is a result of operation of the sensor network. Distributed Energy-Efficient Clustering

(Deec) , Which Is Dedicatedly Designed For Energy

heterogeneous scenarios, where nodes are initialized at various energy levels. However neither of them assures the selection of energy-rich cluster heads, or the evenness of cluster head dispersion. Decentralized Energy Efficient clustering Propagation (DEEP) prevents cluster heads from being too close to each other, but ignores cluster head’s energy qualifications.

In , Lindsey Et Al. Proposed Power-Efficient

Gathering in Sensor Information Systems (PEGASIS).

Communicates With Two Close Neighbors Along The

communication chain. Only a single designated node gathers data from other nodes and transmits the aggregated

Efficient Data Gathering And Aggregation Protocol

(PEDAP), which are near optimal minimum spanning tree based wireless routing scheme. The performance of the

Pedap Was Compared With Leach And Pegasis, And

showed a slightly better network lifetime than PEGASIS.

Manjeshwar Et. Al. Developed A Protocol Called

Adaptive Periodic Threshold-sensitive Energy Efficient

Sensor Network Protocol (Apteen) That Uses An

enhanced TDMA schedule to efficiently incorporate query handling. APTEEN provides a combination of proactive and reactive policies. In the Hierarchy-Based Anycast

Routing (Har) Protocol For Wsns , The Sink

constructs a hierarchical tree by sending packets to

Discover Each Node’S Own Child Nodes In Turn. The

drawback of HAR is that it sends and receives too many packets in the network, expending much energy. Therefore a novel Hierarchy-Based Multipath Routing Protocol (HMRP) is proposed by Wang et. al. to overcome the defects of HAR.

In , Yang Et Al. Proposed A New Routing Scheme;

called Shortest Hop Routing Tree (SHORT), to achieve higher energy efficiency, network lifetime, and more throughput than PEGASIS, and PEDAP-PA protocols.

This scheme used the centralized algorithms and required the powerful base station. Hybrid Energy-Efficient Distributed algorithm (HEED) is capable of setting up a cluster head in the center of a dense area irrespective of network topologies. The probability for each node to become a tentative cluster head depends on its residual energy, and all the tentative heads in which are competing for becoming the final cluster heads.

Developed. Clustering Algorithm Via Waiting Timer

(CAWT) still relies on the local messages exchange to calculate the timer. Backoff strategy clustering scheme , which is advance in selecting energetic nodes as cluster heads and eliminating huddling cluster heads, suggests a novel way to configure the timer. In , Li et.

al.

Clustering

(EEUC) protocol. It divides the nodes into clusters of unequal size. An overhead free fully distributed clustering scheme, called Slotted Waiting period Energy-Efficient Time Driven clustering algorithm (SWEET) is proposed in

. Though Sweet Is An Overhead-Free Method, It

performs even better than some representative clustering schemes in extending system lifetime and enlarging network data capacity.

3. Proposed Model

The proposed routing scheme is based on the fact that the energy consumed to send a message to a distant node is far greater than the energy needed for a short range

Transmission. Here A Simple Radio Frequency (Rf)

transceiver model (Fig. 1) has been considered that is connected to the sensing and processing unit of the sensor node. Data to be transmitted will first pass through the digital-to-analog converter (DAC) and low-pass filter to prepare for up-conversion at the mixer with carrier signal generated by the frequency synthesizer. The subsequent

Modulated Signal Will Be Transmitted By The Power

amplifier (PA) over the wireless channel. Fig. 1 A simple RF transceiver model of sensor node On the receive path, incoming signals will first be amplified by the low noise amplifier (LNA), and then

Of

intermediate frequency (IF) and base-band filters and amplifiers (not explicitly shown in Fig. 1 . In the end, digital data are recovered by the analog-to-digital converter (ADC) and then forwarded to the processing unit for further decoding.

We Have Extended The Leach Protocol By Using A

head-set instead of a cluster head [7} in combination with transceiver model of sensor node . In other words, during each election, a head-set that consists of several

Nodes Is Selected. The Members Of A Head-Set Are

responsible for transmitting messages to the distant base station. At one time, only one member of the head-set is active and the remaining head-set members are in sleep mode. The above communication stages are illustrated in Fig. 2.

The Following Derivation For Transceiver Power

consumption is modeled after those given in , and has been used later for the analysis. On the transmission path, the total power consumption, PTx, can be written as:

(1)

Fig. 2 Communication stages in a cluster of a wireless sensor network

Where Ppa Is The Amount Of Power Consumed By The

PA alone, and PE is the amount of power collectively consumed by the other electronic components such as the mixer, frequency synthesizer, DAC, and various filters.

Determining the exact values for both PPA and PE would depend on RF component design and device technology, which is beyond the scope of this research; though a simple approximation would suffice in the current work.

While PE is generally treated as a constant under various operating conditions, PPA can be further broken down into

(2)

Where PRxSi denotes the receiver sensitivity in Watts, Lo is the path loss attenuation at do metres, Rcom refers to the distance between the transmitter and the receiver in metres, α is the path loss exponent, GTx and GRx represent transmit and receive antenna gains, respectively, and η stands for PA efficiency. In turn, the receiver sensitivity

Level At The Receiver,

NFRx is the noise factor at the receiver, No is the thermal noise floor in a 1 Hz bandwidth (in W/Hz or J), and BW is the channel noise bandwidth (in Hz).

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Where w is the wavelength of the carrier frequency in meters. Substituting Eq.s (3) and (4) into (2), PPA

(6)

On the reception path, the total power consumption,

Prx, Depends On The Power Consumption Of The Lna,

mixer, frequency synthesizer, IF amplifiers, filters, and

Generally The Sensor Model Doesn’T Specify The

transmitting or receiving one bit. Nonetheless, the platform uses transmission rate of 1 Mbps (Rbits) or time to send one bit is 1 µs, so one can calculate the energy required for transmitting one bit, following a method based in the approach presented by Hill et.al in . The energy used in transmitting or receiving one bit and is found by using the power value and can be further derived

Is The Raw Bit Rate, Time In Seconds And

power in Watts.

Described In Is Adopted: For Short Distance

transmission, such as intra-cluster communication, the

Energy Consumed By A Transmitting Amplifier Is

proportional to d2 and for long distance transmission, such as inter-cluster communication, the energy consumption is proportional to d4. Using the given radio and energy consumption models, the energy consumed in transmitting one message among cluster heads for a distance d is given

(9)

Similarly, the energy consumed when the senor node works as a regular (member) node, that is, the energy consumed in transmitting a massage within a cluster for a

(10)

Moreover, the energy consumed to receive the l-bit

Eq. 11 Includes The Cost Of Beam Forming Approach

that reduces energy consumption. The constants used in the radio model are given in Table 1. Table 1: Parameters values used for radio communication model and

3.63 Mw

There is uniform distribution of clusters and each cluster contains n/k nodes. For a sensor network of n

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nodes, the optimal number of clusters is given as k. It is

The Quantitative Analysis Is Based On The Radio

model for shorter distance Eq. 9.

All Nodes Are At The Same Energy Level At The

beginning.

The Amount Of Consumed Energy Is Same For All

the clusters.

•

At the start of the election phase, the base station randomly selects a given number of cluster heads.

•

First, the cluster heads broadcast messages to all the sensors in their neighborhood.

More Cluster Heads And Choose Their Cluster Head

using the received signal strength.

•

Third, the sensors transmit their decision to their corresponding cluster heads.

Their

corresponding nodes.

Chooses A Set Of M Associates, Based On Signal

analysis.

4.1 Election Phase

Using Eq. 8, 10 and Eq. 11, the energy consumed by

As Mentioned In During Data Transfer Phase, The

nodes transmit messages to their cluster head and cluster heads transmit an aggregated messages to a distant base station. The energy consumed by a cluster head is as

(14)

The first part of Eq. 14 shows the energy consumed to transmit a message to the distant base station. The second part of Eq. 14 shows the energy consumed to

K

are not part of the head-set.

−

he energy consumptions in a data transfer stage of

4.3 Starting Energy For One Round

The start energy, Estart, is energy of a sensor node at the initial start time. This energy should be sufficient for at least one round. In one round, a node becomes a member of head-set for one time and a non-cluster head for

(19)

ere it has been assumed that there are k clusters and

H

des. In each iteration, m nodes are elected for each cluster. Thus, in each iteration k m nodes are elected as members of head-sets. The number of iterations required

, Which Is The Number

consists of an election phase and a data transfer stage.

K

of iterations required in one round. Moreover, an iteration

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