End-To-End Communication Between IoT Devices To Maximize Energy Efficiency Through Optimization And Localization Based On The Bio
Inspired Algorithms.
G. Hemanth Kumar1, Dr. N. Gireesh2, C. Somasundar Reddy3
1
Assistant Professor,Department of E.I.E,SreeVidyanikethan Engineering College,Tirupati.
[email protected]
2
Professor,Department of E.I.E,SreeVidyanikethan Engineering College,Tirupati.
[email protected]
3
Assistant Professor,Department of E.I.E,SreeVidyanikethan Engineering College,Tirupati.
[email protected]
Abstract
Network optimization is often defined as a technique for improving network performance in every environment. Every day, huge amounts of data from different types of devices and applications are fed into the network. In this regard, it is clear that an effective solution for optimizing the IoT network must reduce the traffic generated by the Internet of things, thereby degrading other network services and effectively utilizing network resources. Due to the heterogeneity of applications and device types, IoT devices generate traffic than the cellular network. IoT applications generate less data, but control-level messages generate more traffic when the device is integrated into the application.
Therefore, this non-application traffic greatly increases the network load. To overcome this load, therefore, an efficient mechanism is needed for solving and optimizing the message exchange from the control level of the IoT device is required. Network optimization provides several benefits such as faster data transfer rates, data recovery, redundant data deletion, and improve application and network response times. We will confer about the necessity of network optimization and ensure network optimization in the Internet of Things, and then compare algorithms to different network parameters like Throughput, Residual Energy, Normalized Overhead, End to End Delay and Packet Loss Ratio.
Keywords: Network optimization, ACO, PSO, Delay.
1. Introduction
Ant Colony Optimization finds a solution for determining the optimal path from source to destination founded on the real ant behavior. Initially, for finding the food source, the ants move randomly. When the ants find the food source, they move back to the colony. While they move back to the colony, they leave pheromones to represent the food path. Other ants follow the same path to reach the food source. Since several ants move on the same path, pheromone creates a stronger path.
The pheromone amount deposited depends on the quality and quantity of the food. When the food source decreases after a certain time, no pheromones are present in the path. The Ant Colony Optimization finds the shortest and optimal path for IoT data transmission. The Ant Colony Optimization is applicable only when source and destination are predefined. It will not work if paths are not symmetric. Calculate pheromone values based on the number of hops of artificial ants from source to target. When the sensor starts to receive data values, it stores the Destination Address, Next Hop and the routing table stores the pheromone value.The ACO algorithm was developed based on the behavior of the ant colony. It is assumed that the main topic of ant indirect communication to find
the shortest path between food and nest is the development of the ACO algorithm. This property is used to solve discrete optimization problems in IoT. The performance of the ACO algorithm is compared to the particle group optimization (PSO) algorithm. The PSO algorithm models the social behavior of insects and animals. The PSO model is a widely used algorithm to solve set-based probabilistic optimization problems. Both the PSO and ACO algorithms work based on a data clustering algorithm that uses the swarm behavior of the system. If the source and target nodes are specific and predefined, then the comparison of the two ACO algorithms is more efficient. The PSO algorithm is a clustering algorithm used in environments with multiple targets, constraint processing, and dynamic optimization in the network. The ACO algorithm is useful for applications that require more accurate results. The PSO algorithm is implemented when the problem is unclear. The performance of ACO and PSO algorithms was modeled in Network Simulator 2 to determine the performance of the algorithm under fixed environmental conditions. Performance metrics were measured and compared between PSO and ACO algorithms to determine performance in IoT applications.
2. Literature Review
Clustered wsn topology was introduced to reduce the power consumption due to routing packets for longer distance by the individual nodes. Due to the limitation in resources like battery backup, reduction of power consumption plays a important role in WSN applications. A novel joint optimization algorithm is used to achieve higher network lifetime. The optimization algorithm focuses on allocation of power for routing nodes and controls the data rate based on the remaining power of routing nodes [1]. Trade off based optimal scheduling algorithm is introduced to aggregate network data’s with extended lifetime. The network lifetime was promoted by halting the transmission of nodes in the hotspot points of the network. Thus, the data’s are stored temporarily in the node itself and transfer the generated data at once to the sink node at specified time stamp. The power consumption due to data transmission was controlled through reducing sample cycle in the WSN nodes [2].The routing algorithms which performs localization operation does not consider the transmission cost and processing time of the algorithm. DV-max Hop localization algorithm is used along with the routing algorithms to improve the efficiency and accuracy of the location information in the transmitted data. The localization error was reduced by using multi objective optimization functions. Anchor nodes are used to locate the nodes in wireless sensor network [3].Reliability and timeliness parameters are also introduced to maintain quality of service in the network. The control and data packets are transferred in different communication schemes [4].Ant colony optimization algorithm is implemented on a node trust evaluation model. Developed the model using D-S evidence theory, which increases the security level in wireless sensor network applications. The reliability of the D-S evidence theory was improved by providing the intensity of the conflicts generated by the malicious nodes in the network. Trust values are generated for the nodes in the network [5].The expected transmission cost (ETC), availability of bandwidth (EAB) is measured using opportunistic algorithm. The availability of local bandwidth, probability of link delivery, prioritizing rules and the state of forwarding nodes are considered to measure EAB and ETC. The transmission cost and bandwidth are used to generate the priority rate for the routing nodes. Bandwidth control and admission control mechanism are used to regulate the traffic flow, manage bandwidths and assure the delivery of data packet by opportunistic routing algorithm [6].Load balancing is performed to regulate uniform power consumption in the network. Evolution game theory is used for network load balancing. Classic game theory is used to select routing nodes to balance the power consumption of the network. This method helps the GTEB algorithm manage loads and extend network life in wireless sensor networks [7].An energy-efficient routing algorithm is implemented to extend the life
of the wireless network with limited power. The hybrid multicast routing algorithm was introduced to optimize the multicast traffic model. The hybrid algorithm is a combination of a flat-hop routing algorithm and a hierarchical multi-hop routing algorithm. The flat multi hop algorithm manages long distance communications and the hierarchical multi hop algorithm helps the data aggregation process [8].Mobile nodes are independent of complex motion control algorithms and an overall optimization structure is built to measure the energy consumed by mobility and data transmission. Three algorithms are created to create the optimal routing tree, improve the routing tree in an avaricious way, and third, manage nodes in the network without changing the network topology[9].Dynamic clustering protocols provide efficient and secure routing in wireless sensor networks. The cluster head is selected according to the SNR value and energy level of the selected node. Added error correction algorithm to prevent errors between end-to-end transmissions. Receiver-based routing pattern analysis is performed to identify malicious nodes on the network [10].Wireless sensor networks have introduced wireless recharging algorithms and mobile bind-based data collection to control power consumption and power recharging periods at sensor nodes. Initially, the deployment strategy for the anchor node is used to monitor the node's energy consumption, energy balance, channel cost, and battery capacity of the sensor node. The sub algorithm is used to measure the time distribution of the sensor nodes to recharge the battery. Each binding node has its own time-sharing algorithm [11].For themed applications such as structural health monitoring, industrial machine failure analysis, and volcanic tomography, a centralized algorithm is required for aggregating lossless data at the receiving node. In wireless sensor networks, network processing is performed to avoid large data transfers. To improve the performance of these applications, we introduced a lossless processing algorithm in the network.
This algorithm should be able to be separated based on the network topology and efficient routing procedures for each section of the network. These conditions are called partitioning issues due to topology constraints and path design issues [12].
3. Proposed Work
Ant colony optimization (ACO) algorithm was developed from inspiration over the behavior of ants. The algorithm was based on the functional approach of parallel processing over the thread and applying the solutions based on the previous threads knowledge. From various threads, the collective behaviorappears. Asynchronous agents and parallel computing sets move the state of the problem according to the limited solution of the problem to solve the problem. They move using local decision-making policies and rely on two parameters called attraction and trail. As each ant moves, it creates a solution to the problem. When an ant reaches a solution or process, Ant measures the solution used to change the trail value of the component. Pheromone data will be used to detect ants in the future. In addition, two mechanisms are added to the ant colony algorithm. The first is the evaporation of the trail and the actions of the daemon. Evaporation of trails will reduce all trace values over time, so avoid the infinite accumulation of traces on components. Daemon operations can be used to perform centralized operations. However, central intervention measures are not implemented by individual ants. For example, global data update or local optimization process call can be used to choose whether to switch the detection process from a non-local perspective.
________________________________________________________________________________________
Algorithm 1: Ant colony optimization Algorithm Pseudo code
__________________________________________________________________________________
1: The function to determine the routing path is represented by R ∈f(x) : x ∈Rn 2: The probability that a routing path exist is represented by τ i
3: While (node initialization) 4: do (iteration for n number of nodes)
5: for (b=1 to b=n nodes) 6: do (route construction) 7: s0 ← ∅
8: for (l=1 to n node)
9: select optimal path among ant to transmit data 10: si ← si−1 ∪ {xi }
11: endfor 12: endfor
13: Li –select the best iteration among routing path.
14: LG- Select the best routing path for data transmission.
15: τ ←Route table updating based on packet delivery 16: endwhile
_______________________________________________________________________________________
3.1. Particle Swarm Optimization Algorithm
The Optimization Algorithm uses a number of particles to discover the solution space from an objective function. In solution space, every particle denoted as a solution vector. In each iteration, the solution vector can be updated by optimization algorithms in order to discover the optimal solution. In the search space, each particle is formed as a position vector and random velocity at the beginning.
Each particle has a previous memory with the best value of the objective function and equivalent previous better position vector.
.
Figure 3. Movement of a particle
In a swarm, each particle knows the better global value of the objective function between better global position vector and corresponding to the entire particles. When the optimization process occurs, every particle moves stochastically in the direction of the global best solution and previous position of the particle is shown in figure 3. Particles tend to move in the direction of better positions in every iteration and procedure repeats until the entire particles converge into an optimal solution.
This algorithm imitates the social behaviour of birds flocking and fishes schooling. Starting from a randomly distributed set of nodes, the algorithms try to enhance the solution corresponding to a quality measure. The enhancement is performed by moving the control packets around the search space through some simple mathematical expressions that design some inter-node communications.
These mathematical expressions trigger each control packet’s movement toward its own best- experienced path and the swarm’s best path so far, along with some random deviations. There are many different variants with distinct updating rules.In the case of IoT, the multiple paths are encoded as particles. The main aim of Particle Swarm Optimization in IoT is to find the particle with maximum bandwidth related with the particle’s link. In PSO every particle has a current position (position of particle existence in current time), personal best position, global best position (best position of any particle among all particles in search space). The best position of a particle is called as
pBest and the best position comparison with all other particles is referred to as gbest. In the PSO each data packet try to move towards particle which is close to the solution and after a certain time every data packet stop to the particular solution which is the optimal solution. In PSO, the sensor nodes must be defined initially. This gives a more accurate result if sensor nodes are spread out over the network so far so that initially path can be found from all space of the network. These initial paths break network in a small search space.
_________________________________________________________________________________
Algorithm 1: PSO Algorithm Pseudo code
_________________________________________________________________________________________
1: for (n=1,2 . . . , m) 2: for (l=1,2 . . . . , n)
3:𝐾 = [ 1 + 𝑏 − 1 𝑋𝑟𝑎𝑛𝑑 + 0.5]
4: L = 𝑝𝑖𝑗𝑏𝑒𝑠𝑡
5: B={0. C lv I, t best } 6: If (b==1)
7: A= 1; 𝑝𝑖𝑗𝑏𝑒𝑠𝑡 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 𝑋𝑟𝑎𝑛𝑑 + 0.5]
12: I=0 if (𝑝𝑖𝑗𝑏𝑒𝑠𝑡<1.0) 13:while (n=1) 14:𝑝𝑖𝑗𝑏𝑒𝑠𝑡<1.0= Y 15: end if 16: end if 17: end while 18: end for 19: end for
4. Experimental Results
The network was framed with maximum coverage region of 2000 square meters and the network connection was developed using WiFi standard. The sensor nodes are initially powered about 100J.
Communication was established between the nodes for about 16 seconds. The performance of the optimization protocol was recorded for 30 nodes with a simulation time of 16 seconds. TCP communication with CBR traffic regulator was generated between the sensor nodes to transfer the data from sender to receiver nodes. The optimization algorithm manages the packets passing through the routing nodes for maximum network efficiency. Network performance is measured using bandwidth graphs. Bandwidth represents network speed. Maximum bandwidth consumed by the network at a fixed time interval is plotted in the unit of kb/s. Figure 4.1 shows the throughput plot for PSO and ACO optimization algorithms. ACO algorithm initiates ant systems to identify the power- efficient path between the sender and receiver nodes. Thus control packets are transferred every time the communication link is generated between the sensor node thus it reaches maximum throughput than the PSO algorithm.
Figure 4.1. Throughput
Figure 4.1 shows the packet forwarding rate of the network. At the initial state, both PSO and ACO algorithm generates equal Packet Delivery Ratio values. After 6th seconds the communication links within the network increases were ACO algorithm generates efficient packet delivery ratio than the PSO algorithm. The packet delivery ratio plot does not represent the retransmission rate. Thus, the ACO algorithm generates a maximum delivery ratio of 57% and PSO algorithm achieves 55%.
Network bandwidth is affected by latency, packet loss, and network jitter.
Figure 4.2. Packet Delivery Ratio
Figure 4.3 shows the packet loss ratio plot. In which the ACO algorithm achieves the maximum packet loss of about 0.11% and PSO achieves 0.13 %. The ACO algorithm achieves 17%
lesser packet drop than PSO algorithm. The throughput, packet loss ratio, packet delivery ratio and residual energy were measured with reference to simulated time with unit seconds.
Figure 4.3. Packet Loss Ratio
Figure 4.4 represents the delay measured in the network after implementing PSO and ACO
algorithm. ACO algorithm initially generates higher delay than the PSO algorithm due to the functioning of the ant system. More control packets were transferred between the nodes to find the optimal route. Until the optimal route is identified the delay will be greater, once the optimized path is identified then the delay will be made constant with slight variations.
End-to-end delay is implemented by changing the size of packets transmitted between optimized paths. The pheromone value increases with the size of the transmitted packet. Increase in pheromone value increases the network delay t find the most optimal path with the node with higher energy backup to execute the transmission without path loss. The end-to-end delay was greater up to packet size of 200 bits and then the value is made more or less constant. A maximum delay of 2ms is generated in AOC algorithm and 2.5 ms for the PSO algorithm.
.
Figure 4.4. End to End Delay
Figure 4.5 shows the residual energy consumed by the network by implementing PSO and ACO algorithm. The ACO algorithm consumed the energy of about 0.55joules and PSO algorithm consumes 0.75joules. PSO algorithm collects more swarm information’s from neighbour nodes. In the case of cluster-based topology the swarm data will be limited but in flat grid network more data to be analysed to perform communication between the sensor nodes. Simulation is performed using flat grid topology and possesses the power rate of 0.75mJ for Tx power, 0.25mJ as Rx power, 0.04mJ as idle power and 0.3mJ as sensing power. Residual energy also affects network efficiency. As power consumption increases, the probability of an initial square node increases. Early dead nodes shorten network life.
Figure 4.5. Residual Energy
The overhead is a measure of the number of control packets sent between nodes to establish a
communication channel between nodes. The ACO algorithm sends an ant packet to identify an optimized path between the sender and receiver nodes. The ACO algorithm finds some optimized paths to overcome path loss due to energy loss due to the frequent use of the same path. The ACO algorithm generates a maximum normalized overhead of about 1.35 for the maximum packet size of 300 bytes. PSO algorithm generates normalized overhead of 1.2. Figure 4.6 shows the normalized overhead for the network using PSO and ACO algorithm.
Figure 4.6.Normalized Overhead
5. Conclusion
Optimization problems such as deployment, design, planning and implementation of IoT create a multipurpose optimization technique to solve the problem. Each method has its own advantages in the approach to the optimization problem, so decision makers must choose one method with the largest damage solution based on the problem. These multiple goals for the same issue may or may not be contradictory. In the design, operation, deployment, deployment, and planning of wireless sensor networks, the type of optimization solution is chosen based on the type of optimization operation. The algorithmic details of each algorithm implemented in the IoT environment are analyzed. The advantage of Ant colony optimization is discussed by comparing the performance with the Particle Swarm Optimization algorithm. This solves the energy constraints created by sending additional control packets in the Internet of Things.
REFERENCES:
[1]Z. Liu, Y. Yuan, X. Guan, and X. Li, “An approach of distributed joint optimization for cluster-based wireless sensor networks,” IEEE/CAA J. Autom.Sin., vol. 2, no. 3, pp. 267–273, 2015.
[2] J. Zhang, J. Long, A. Liu, and G. Zhao, “An optimal schedule algorithm trade-off among lifetime, sink aggregated information and sample cycle for wireless sensor networks,” J. Commun. Networks, vol. 18, no. 2, pp. 227–237, 2016.
[3] F. Shahzad, T. R. Sheltami, and E. M. Shakshuki, “Multi-objective optimization for a reliable localization scheme in wireless sensor networks,” J. Commun.Networks, vol. 18, no. 5, pp. 796–805, 2016.
[4] W. Zhang, Y. Liu, G. Han, Y. Feng, and Y. Zhao, “An Energy Efficient and QoS Aware Routing Algorithm Based on Data Classification for Industrial Wireless Sensor Networks,” IEEE Access, vol. 6, no. 5, pp. 46495–46504, 2018.
[5] Z. Sun, Z. Zhang, C. Xiao, and G. Qu, “D-S Evidence Theory Based Trust Ant Colony Routing in WSN,” Commun.
Syst. Theor., vol. 6, no. 3, pp. 27–41, 2017.
[6]P. Zhao and X. Yang, “Opportunistic Routing for Bandwidth-Sensitive Traffic in Wireless Networks with Lossy Links,”
J. Commun.Networks, vol. 18, no. 5, pp. 806–817, 2016.
[7] M. A. Abd, S. F. M. Al-Rubeaai, B. K. Singh, K. E. Tepe, and R. Benlamri, “Extending Wireless Sensor Network Lifetime with Global Energy Balance,” IEEE Sens. J., vol. 15, no. 9, pp. 5053–5063, 2015.
[8] A. E. A. A. Abdulla, H. Nishiyama, J. Yang, N. Ansari, and N. Kato, “HYMN: A novel hybrid multi-hop routing algorithm to improve the longevity of WSNs,” IEEE Trans. Wirel. Commun., vol. 11, no. 7, pp. 2531–2541, 2012.
[9] F. El-Moukaddem, E. Torng, and G. Xing, “Mobile relay configuration in data-intensive wireless sensor networks,”
IEEE Trans. Mob. Comput., vol. 12, no. 2, pp. 261–273, 2013.
[10] S. Ganesh and R. Amutha, “Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms,” J. Commun. Networks, vol. 15, no. 4, pp. 422–429, 2013.
[11] S. Guo, C. Wang, and Y. Yang, “Joint mobile data gathering and energy provisioning in wireless rechargeable sensor networks,” IEEE Trans. Mob.Comput., vol. 13, no. 12, pp. 2836–2852, 2014.
[12]P. Guo, X. Liu, J. Cao, and S. Tang, “Lossless In-network Processing and Its Routing Design in Wireless Sensor Networks,” IEEE Trans. Wirel.Commun., vol. 5, no. 3, pp. 1–15, 2017.
[13] J. Habibi, A. G. Aghdam, and A. Ghrayeb, “A framework for evaluating the best achievable performance by distributed lifetime-efficient routing schemes in wireless sensor networks,” IEEE Trans. Wirel.Commun., vol. 14, no. 6, pp. 3231–
3246, 2015.
[14] S. He, K. Xie, W. Chen, D. Zhang, and J. Wen, “Energy-Aware Routing for SWIPT in Multi-Hop Energy-Constrained Wireless Network,” IEEE Access, vol. 6, no.DC, pp. 17996–18008, 2018.
[15] F. Liu, Y. Wang, M. Lin, K. Liu, and D. Wu, “A Distributed Routing Algorithm for Data Collection in Low-Duty-Cycle Wireless Sensor Networks,” IEEE Internet Things J., vol. 4, no. 5, pp. 1420–1433, 2017.
[16] F. Mansourkiaie and M. H. Ahmed, “Joint Cooperative Routing and Power Allocation for Collision Minimization in Wireless Sensor Networks With Multiple Flows,” IEEE Sens. J., vol. 16, no. 5, pp. 1398–1411, 2016.
[17] M. Zhao and Y. Yang, “Optimization-based distributed algorithms for mobile data gathering in wireless sensor networks,” IEEE Trans. Mob.Comput., vol. 11, no. 10, pp. 1464–1477, 2012.
[18] M. Zhao, J. Li, and Y. Yang, “A framework of joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks,” IEEE Trans. Mob.Comput., vol. 13, no. 12, pp. 2689–2705, 2014.
[19] A. E. Zonouz, L. Xing, V. M. Vokkarane, and Y. L. Sun, “Reliability-oriented single-path routing protocols in wireless sensor networks,” IEEE Sens. J., vol. 14, no. 11, pp. 4059–4068, 2014.
[20] J. W. Lee and J. J. Lee, “Ant-colony-based scheduling algorithm for energy-efficient coverage of WSN,” IEEE Sens. J., vol. 12, no. 10, pp. 3036–3046, 2012.
[21] G.L. da Silva Fré, J. de Carvalho Silva, F.A. Reis, L.D.P. Mendes, Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things, in International Workshop on Telecommunications (IWT), 2015, pp. 1–7.
[22] L. Song, K.K. Chai, Y. Chen, J. Loo, S. Jimaa, J. Schormans, Qpso-based energy aware clustering scheme in the capillary networks for internet of things systems, in IEEE Wireless Communications and Networking Conference (WCNC), 2016, pp. 1–6.
[23] W.-T. Sung, C.-C.Hsu, Iot system environmental monitoring using IPSO weight factor estimation, Sens. Rev. 33 (3) (2013) 246–256. [20] T. Kumrai, K. Ota, M. Dong, J. Kishigami, D.K. Sung, Multi-objective optimization in cloud brokering systems for connected internet of things, IEEE Int. Things J. 4 (2) (2017) 404–413.
