REDUCING POWER FEASTING AND EXTEND NETWORK LIFE TIME OF IOT DEVICES THROUGH LOCALIZATION

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REDUCING POWER FEASTING AND EXTEND NETWORK LIFE TIME OF IOT DEVICES THROUGH LOCALIZATION

G.HEMANTH KUMAR¹, RAMESH G.P²

1Research Scholar, Department of E.C.E, St. Peter's Institute of Higher Education and Research, Avadi, Chennai

2Professor& Head, Department of E.C.E, St. Peter's Institute of Higher Education and Research, Avadi, Chennai.

Abstract

Current advanced technologies require application-level gateways to offer connectivity to specific IoT devices.

This concept involves mapping the physical and virtual worlds. An IoT-based system is a complex and dynamic combination of objects that typically have no distributed control. The Swarm Intelligence system is a distributed, self-organizing algorithm used to solve complex problems with limited computational capabilities and dynamic properties. This is accomplished by using a unique addressing scheme to connect devices with capture capabilities and sending acquired data to cloud. Since many IoT devices are associated to the Internet, need to place many IoT gateways in the communication network. Because these IoT devices generate vast amounts of data, routing in the IoT network becomes a problem. Thus, to overcome this problem, there is a need for an efficient mechanism for resolving and optimizing message exchange from the control level of IoT devices.

In this paper, Measures concert metrics, results of EX-OR shows that the algorithm works well compared to PSO and EX-OR can be protracted to resolve the problematic of different network parameters

Keywords: Gateway, Delay, Energy efficiency, opportunistic routing (OR), Practical Swarm optimization (PSO) 1. Introduction

The Global best version of PSO have faster information transfer by the search space due to the large inter- connectivity and effective communication among entire particles in the swarm. So that, the global PSO converges faster then compared with the local PSO. In addition, the global best PSO have feasibility to converge into a local optimum. So, the global version sometimes give the inaccurate solutions compared with the local version of PSO. The Extremely Opportunistic Routing (ExOR) protocol uses the advantages of wireless sensor networks. The number of hops has predicts the useful forwarder by using the ranking forwarding nodes. In sequence nodes, the EXOR can forwards a packet. The next forwarding node is calculated after a previous node send it to packet.When the previous nodes sent its packet, the next forwarding node is calculated.

One node get the packet from all nodes. It also appears to have fewer connected devices per person. The reason is that the calculations are based on the entire world population, many of which are not yet connected to the Internet. By actually reducing population samples to people connected to the Internet [1]. To improve the connectivity of IoT devices just as Wi-Fi access points have revolutionized notebook utilities, we assume that deploying IoT gateways around the world can revolutionize application independence [2]. The load is a critical amount to investigate the network performance of large WSN that are expected to support ubiquitous applications such as custom healthcare and smart home in a support environment (eg, the Internet on the Internet). In this environment, because targeting is not a practical approach, nodes are typically randomly assigned. The average load of a point is considered to be a load of reduced (or reduced) load to this point, while it is the definition of the average load of the area based on statistical predictions [3]. However to afford a huge number of IoT network gateways to cover and connect to all IoT devices, you can use less IoT gateways and use only a portion of the interface. This method is inefficient due to the high cost of IoT gateways. In addition, network gateways pay for multiple connections, which is expensive to connect individual IoT devices directly to the IoT cloud through IoT gateways. Therefore, every point in the hotel chain requires an Internet connection

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shared by all devices that use a low-cost information exchange gateway [4]. Connecting low-power IoT devices to the Internet via an IoT network resulted in gateway issues. It is believed that our routing approach is a cost- effective solution to this gateway problem in IoT networks. The performance of an opportunistic routing algorithm (OR) was estimated by implementing a planar network with randomly located nodes. A network with a maximum coverage area of 2000 m is considered.

2. RELATED WORK

Existing biologically inspired networks and communication protocols and algorithms designed with Information about biological systems, which is a source of stimulation designed to simulate the rules and mechanisms that govern these systems, is presented along with open questions from research in biologically induced networks.

Systems and processes have their own characteristics, such as their adaptability to changing conservational situations. [6]. The scheduling improves the efficiency of the entire IoT system. In addition, the optimal management of faulty nodes has become an important aspect of the scheduling algorithm has been proposed in order to continuously maintain the continuous service flow provided by these sensors. Recovery starts with a timely detected error or error exchange process. To determine the number of equivalent surrogate nodes positioned in the wireless sensor network, consider the cost of providing energy and standby environments as node failures with scarce resources in the primary environment [7]. Authors proposed routing protocol combines the authentication and routing process without significant overhead by embedding multilevel parameters in the routing algorithm [8]. A routing algorithm that creates and maintains a DODAG for transferring data from the sensor to the root directory in one direction. However, owing to the limited resources of the sensor node and low reliability of the wireless connection, routing cannot be considered an effective way to see the presentation requirements of other applications in the same way. Problem Concentrated solution with multipath RPL routing protocol to many individuals and groups.The approach has achieved better energy efficiency. Compared to traditional RPL solutions, packet delivery rate end-to-end delay and network load balancing [9]. We provide a multi-path routing metric (DMRM) based on a calculated variety based on different conditions of the wireless network. In addition to detecting unstable stream of traffic loads on the line, the routing metric takes into account the number of link losses, the in-flow and inter-flow interference, channel separation and the several data rates of the nodes, when selecting multiple paths. DMRP is designed to improve performance and reduce packet latency by choosing the optimal route in terms of packet rate and load balancing multiple routes [10].

3. System Model

Communication of sensor nodes from one node to another requires various steps related to the transmission of data packets. Data from the sensor nodes pass through the gateway and then stored on the server. Capturing all sensor data together through the server's gateway node requires a protocol that is proficient for bandwidth use on previous connections. It should be stable and energy efficient with limited equipment. The algorithms in this class address common and critical problems in large networks characterized by dynamic, self-configurable characteristics, limited resources, and lack of centralized management and infrastructure. The IoT entails of variety of devices that exchange and transmit information, which keeps increasing traffic..D2D communications can be performed autonomously using the network infrastructure or is autonomously licensed or unauthorized.

To overcome energy constraints, optimization algorithms have been introduced in IoT. Optimization and practical swarm optimization algorithms not only manage energy efficiency but also use limited resources to improve network performance. The opportunistic routing algorithm uses more nodes to send packets to routed nodes. Easy routing continues to use location data, but the transport node is chosen differently depending on the protocol used. The benefits of opportunistic algorithms arise in dense networks. Other sites may have many shipments. Opportunistic routing algorithms provide great opportunities for neighbours.

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Figure1. System model for internetwork data exchange between IoT gateways and Sensors 4. Problem Statement

Extremely opportunistic routing (ExOR) consists of three steps: selecting a forwarding candidate, verifying the transmission, and determining if the received packet should be forwarded. Each node in the network is assumed to contain a matrix that approximates the loss factor for straight wireless communication. The first EXOR forwarding sequence node may receive a subset of candidates from all neighbouring nodes and receive packets nearer to their target. The source inclines this amount in the packet header and prioritizes each node after deleting it.

The recipient receiving the packet retrieves the address from the list of candidates in the header. Each receiver delays the time specified at that position in the incline formerly sending a confirmation [19]. This procedure is recurrent until the final target receives the packet. Opportunistic transmissions can lead to duplication, to avoid duplication due to scheduling. Instead of selecting a fixed sequential route (such as src->

B-> D-> dst) for background processes to periodically collect ETX information, the source uses a metric, such as ETX, to select the forwarder list (the forwarders list of the packet). The link state flood forwarder is prioritized by metrics such as ETX on the destination. The highest priority forwarder is sent at the end of the stack. The remaining carriers will be shipped in priority order. Each carrier delivers packages that have not yet been received from the high-priority forwarders contained in the deployment map.

A. Extremely Opportunistic Routing Algorithm

The opportunistic routing algorithm uses more nodes to send packets to routable nodes. Opportunistic routing still uses location data, but transport nodes are chosen differently depending on the protocol used in each case. Advantages of the opportunistic algorithm appear in dense networks. There are many potential shipments to other nodes here. Opportunistic routing algorithms provide tremendous power to the neighbours. Efficient routing algorithms should use inter-layer network level and MAC layer integration. The network layer creates a list of available candidate nodes and sends them to the MAC layer. The last solution obtained at the MAC level corresponds to channel conditions, node connectivity, reliability, and so on.

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Algorithm 1: Extremely Opportunistic Routing Algorithm:

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1: Transmission of data among nodes in network

2: Select data forwarding path with nodes F(h) and N(h);

3: The nodes represent by i ∈ N (h) do 4: for each node m ∈ L do

5: for each node c ∈ V where c not equal to m do 6: for each node i ∈ N(h) do

7: 𝐿_𝑎,𝑏= 1_𝑎,𝑏+ 1_𝑎,𝑏^𝑖

8: Determine interconnected nodes in network 9: 𝐶_𝑎,𝑏^𝑖

10: end for

11: 𝐿_𝑎,𝑏= 1_𝑎,𝑏+ 1_𝑎,𝑏^𝑛+1

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12: Compute node coverage region 13: end for

14: Compute mean distance between nodes 15: Determine data packet size to be sent 𝐿_𝐶 16: If 𝐸(𝐿_𝑎,𝑏) >= 𝐿(𝐶_𝑎) ∩ 𝐼_𝑎,𝑐 >= 𝐿_𝐶 then 17: 𝐻 = 𝐻 ∪ {(𝑛, 𝑑)}

18: end if 19: exit for

20: if knot n having the maximum significance to gets data the package successful.

21: retort acknowledgement to inform source;

22: for each knot i ∈ F (h), excluding for n, ensure 23: reset the data packet;

24: exit for 25: otherwise

26: if sending node has not effectively acknowledged the packet then 27: droplet the data packets;

28: otherwise 29: go to 2;

30: exit if 31: return

B. Particle Swarm Optimization (PSO)

The Global best version of PSO has faster information transfer by the search space due to the large inter-connectivity and effective communication among entire particles in the swarm. So that, the global PSO converges faster then compared with the local PSO. In addition, the global best PSO have feasibility to converge into a local optimum. So, the global version sometimes give the inaccurate solutions compared with the local version of PSO Eberhart and Kennedy presented PSO in [12]. In the global particle swarm optimization, every particle i £ [1,2,...n] is named by a velocity vector, position and xi(k) , vi(k) at iteration k.

Also, at iteration k, every particle ‘i’ remembers the better individual position, pibest(k) that it have vistited from the first iteration and the global solution between the entire particles, gibest(k).

In every iteration, each particle ‘i’ best position is updated based on the formula (4)

𝑝_𝑖^{𝑏𝑒𝑠𝑡} (𝑘 + 1) = {

𝑝_𝑖^{𝑏𝑒𝑠𝑡} (𝑘) 𝑖𝑓 𝑓(𝑥_𝑖(𝑘 + 1)) < 𝑓 (𝑝_𝑖^{𝑏𝑒𝑠𝑡}(𝑘)) 𝑥_𝑖(𝑘 + 1) 𝑖𝑓 𝑓(𝑥𝑖(𝑘 + 1)) > 𝑓(𝑝_𝑖^{𝑏𝑒𝑠𝑡}(𝑘))

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Where ‘i’ denoted as the particle index and f(xi(k)) represents the objective function measurements of particle ‘i’ at iteration k and position x. The global positions must be updated either in the basis of the current positions or individual best positions of entire particles in each iteration.

In PSO algorithm, the data is requested by optimal node from other nodes. The request packet sending task by cluster head is denoted by {t1,t2,. . . .tm}. The resource required to perform each task is represented by {r1, . . . . . . . . .rj, rni}. Correspondingly the resource spent for each task is represented by {t1r1, t2r2,… . .tnrn}.

After evaluation of routing path, a optimal route is selected and data send through nodes in the path. The position of nodes is assumed to be stationary and represented by pij.

Algorithm 2: Modified PSO Algorithm Pseudo code

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1: for (n=1,2 . . . , m) 2: for (l=1,2 . . . , n)

3: 𝐾 = [(1 + 𝑏 − 1)𝑋 𝑟𝑎𝑛𝑑( ) + 0.5]

4: L = 𝑝_𝑖𝑗^{𝑏𝑒𝑠𝑡}

5: B={0. C lv I, t best }

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6: If (b==1)

7: A= { 1; 𝑝_𝑖𝑗^{𝑏𝑒𝑠𝑡} is same even when there is node missing in selected routing path 0; 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 8: If (a==1)

9: N=1.

10: Do

11: 𝑌 = [(1 + (𝑉 + 1)𝑋 𝑟𝑎𝑛𝑑( ) + 0.5]

12: I=0 if (𝑝_𝑖𝑗^{𝑏𝑒𝑠𝑡}<1.0) 13: while (n=1)

14: 𝑝_𝑖𝑗^{𝑏𝑒𝑠𝑡}<1.0= Y 15: exit if

16: exit if

17: exit while 18: exit for

19: exit for 5.Network Simulation

The performance of the opportunistic routing algorithm (OR) was evaluated by the implementation of Network Simulator 2 (NS2). Planned networks with randomly placed nodes are considered. A network of up to 2000 meters is considered. The sensor node is initially supplied with 100 lines. Table shows the network parameters assigned to NS2 to assess the concert of opportunistic routing algorithms. The concert metric of opportunistic routing algorithm is related to the concert of the particle swar optimization algorithm.

Table1. Network Simulation Parameters

Network Parameters Values assigned

MAC Type 802_11

Antenna Omni Directional Queue Type DropTail/PriQueue

No. of Nodes 30

Queue Length 50 to 300 bytes Maximum Network Size 2000 * 2000 meters

Routing Protocol OR & PSO

Simulated Time 16 Sec.

Channel Type Channel/Wireless

Traffic source CBR

Initial Energy 100J

Tx. Power 0.75mJ

Rx Power 0.25mJ

Idle Power 0.04mJ

Sensing Power 0.3mJ

6. PERFORMANCE EVALUATION IN DIFFERENT METRICS

Bandwidth is a measure of the maximum bandwidth a network spends for a fixed period of time.

Bandwidth is measured in kbps. Network performance is measured using network bandwidth. Bandwidth is the maximum rate that a network reaches to transmit data packets between nodes. Increased loss of information and one way delay affect throughput growth. Figure 2. Shows the throughput plot measured using opportunistic algorithm and particle swarm optimisation algorithm in IoT environment. The OR algorithm generates a maximum throughput of about 240Kb/s and the PSO algorithm generates 210kb/s. The opportunistic routing algorithm does not handle many control packets to identify the optimum path in the network and it works in the

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distributed fashion. Every node in the network performs opportunistic routing algorithm so forwarder node will decide the next hop forwarder so complexity in executing the algorithm is less which increases the throughput of the network.

Figure 2. Throughput

Residual energy is the maximum power consumed by the network through a fixed simulation time.

Maximum energy of 100joules is assigned for wireless sensor nodes. Each sensor nodes was assigned with Tx power of 0.75mJ, Rx power of 0.25mJ, processing power of 0.04mJ and sensing power of 0.3mJ. Figure 3 validates the remaining energy of the set-up by applying opportunistic routing algorithm and PSO algorithm.

Opportunistic routing algorithm consumes maximum power of 0.65Joules and PSO algorithm consumes maximum power of 0.7joules for simulation time of 16 seconds. PSO algorithm handles more swarm information collected from sensor nodes to make decision over the optimal path. PSO algorithm consumes more energy for processing the collected swarm data’s than the opportunistic algorithm.

Figure 3. Residual Energy

Packet delivery rate is a ration of the number of packets effectively communicated on each data transfer. Packets retransmitted due to network loss are not included. Figure 4 shows the measured packet loss rates for PSO and opportunistic routing algorithms in a IoT. The ratio of packets decreases when the packet descent increases owed to path failure or occurrence of congestion in the network. Opportunistic routing algorithm makes decision at intermediate forwarder nodes. Thus, occurrence of path failure or packet loss due to congestion can be avoided or resolved at intermediate forwarder node itself. Thus packet delivery ratio for opportunistic routing algorithm is achieved about 56.7% and the PSO algorithm achieves 55.4%.

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Figure 4. Percentage of successfully received packets

The ratio of the total number of packets sent and number of packets lost is a extent of number of packets failed to deliver to the receiver node. The packet loss may increase due to poor transmission link status and congestion in the intermediate communication link. Packet loss ratio includes number of retransmission rate during transmission of data packet through the selected data path. Figure 5 characterizes the packet loss ratio measured using PSO and opportunistic routing algorithm with simulation time of 16 seconds. The intermediate forwarder selection mechanism decreases the packet loss ratio of about 0.12% for opportunistic routing algorithm and PSO routing algorithm achieves 0.14%.

Figure 5. Packet Loss Ratio

The delay measured in unidirectional communication of data packets amongst relay knots is expressed as a one way delay. The interval increases as the processing time of the control packet increases during the selection for the next hop node, and the transmission rate increases because the transmission relation between the forwarding knots is not good. The PSO algorithm is a centralized algorithm that makes decision-based decisions.

The PSO algorithm generates delays while processing data during the initial transmission. Once the path is identified, the data packet is transmitted through the completion of the data transmission over the selected path.

Figure 6 shows the measured one way delay in IoT using PSO and opportunistic routing algorithms. The opportunistic routing algorithm generates a maximum delay of 2ms and the PSO generates a maximum delay of about 2.5ms.

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Figure 6. One way Delay

The rate of communication of control packets between the forwarder nodes to establish reliable communication path is referred as overhead in the network. PSO algorithm consumes more control packets to collect knowledge based on swarm intelligence when compared to opportunistic routing algorithm. The path selection using swarm intelligence generates more efficiency when the network traffic is constant all over the network. In case of larger network opportunistic routing algorithm provides better performance than the PSO algorithm. Figure 7. shows the normalised overhead measured using PSO and opportunistic routing algorithm.

The PSO algorithm generates the overhead value as 1 and the opportunistic algorithm generates overhead of 0.8.

Figure 7. Normalised Overhead 7. Conclusion

IoT is widely used to surveillance and control applications in everyday life. However, most sensor nodes have a limited battery. Therefore, one of the biggest hurdles in the development of the routing protocol is energy saving optimization. This article focuses on reducing power feasting and extending network life. In this case, we use the experience of the OR model to enhance the power proficiency of the network, taking into account the sensor nodes in positions of distance to the receiver and remaining energy of both. The algorithmic details of each algorithm implemented in the IoT environment are analysed. The performance of the Particle Swarm Optimisation algorithm and Opportunistic routing algorithm was compared with each other. Experimental

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results depict that EX OR algorithm can not only increase convergence rate but also significantly reduce packet loss rate, residual energy and one way delay related to particle swarm optimization (PSO) and its other variants.

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