A Power Balance Control Strategy of Wireless Sensor Network with Collaborating Heuristic Jie Yu1 , Thi-Kien Dao , Truong-Giang Ngo3(B) , and Trong-The Nguyen2,4 College of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou 350118, China Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China Faculty of Computer Science and Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam giangnt@tlu.edu.vn Department of Information Technology, Haiphong University of Management and Technology, Haiphong, 18000, Vietnam Abstract In order to effort a strong enough signal, nodes in wireless sensor networks (WSN) have to increase their transmission power that continues to maintain the transmission power However, a vicious circle is iterated that causes a decline in the overall network performance, low utility, and network life cycle shorten This paper presents a solution to the power balance control strategy for WSN with collaborating heuristic A distance between the weight factors and obtained nodes interference value is used to establish a useful interference model for enhancing the signal-to-interference-noise ratio (SINR) The utility function of the nodes residual energy and transmission rate is modeled by apply heuristic strategy The optimal transmission power is obtained after several iterations of the heuristic algorithm Simulation results show that the proposed approach can prolong the network life cycle and achieve higher network utility Keywords: Wireless sensor network · Transmission power · Collaborating heuristic Introduction Wireless sensor networks (WSN) composed of several nodes have integration, selforganization, and multi-hop, etc., [1, 2] The nodes can perceive, collect, and transmit the surrounding information through cooperation to realize the monitoring of the treatment of the test area [3, 4] WSN has become an essential part of the internet industry with great convenience to people’s life and study, e.g., reflected in the smart home, intelligent transportation, and other aspects [5] However, nodes cannot be replaced with batteries as the massive scale of sensor nodes, and premature death of the nodes will lead to the change of topology structure such as routing [6] The improper transmission power of the nodes will accelerate the paralysis of the network and reduce the network utility and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021 J.-S Pan et al (eds.), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies 212, https://doi.org/10.1007/978-981-33-6757-9_42 332 J Yu et al life cycle [7] It means improving the energy use efficiency of nodes and extending the network life cycle are the key technologies that need to be solved urgently in WSN [8] Effective power control is the precondition for WSNs and nodes to perform persistent work [9] Clustering WSNs have a lot of success in deserving energy effective sensor networks [10] However, the problems of node interference have not been much considered comprehensively [5] In this paper, the short life cycle and low network utility caused by improper transmit power are dealt with by establishing a useful interference model to obtain active interference between channels and optimizes the game framework by using a utility function In this way, each node gets the corresponding optimal transmit power to reduce node energy consumption, extend the network life cycle, and achieve optimal system performance Related Work The heuristic is often used to deal with the selection of strategies in the process of mutual cooperation or competition by adjusting the behavior of participants to maximize the benefits at the minimum cost In order to simplify the modeling process, the following objects are set for the research object and the corresponding environment [2] • Network nodes are randomly distributed and then remain stationary Sink nodes are located in the center of the whole region, and their energy is not limited • The nodes can perceive the position and transmitting the power of each node within the communication radius • All nodes have the same initial information, including energy, transmitting power, perceived radius, etc., and the transmitting power of nodes is controllable Assume that the number of randomly distributed sensor nodes in the monitoring area is N The node j, which is only within the perceived radius R of node i, will interfere with it, and the following useful interference model is established N pj gij αij + η2 Ii = (1) j=1 j=i where pj represents the transmit power of node j, gij is the link gain of node i and node j, N j=1,j=i pj gij αij represents the sum of interference of node i by other working links in one data receiving period, η2 is channel noise When the distances of nodes are different, the degree of interference is also different To improve the accuracy of practical interference calculation, set αij as the interference weighting factor, namely αij = exp − D R (2) D= (xi − xj )2 + (yi − yj )2 where D represents the Euclidean distance of node i and node j It can be seen that when the node distance increases, the influence weight and interference value of node j on A Power Balance Control Strategy of Wireless Sensor Network … 333 the link gradually decrease Based on the above analysis and the definition of SINR in literature, the improved SINR model is presented as follows SINR = W Ri (pi , Ii ) pi gi N j=1 pj gij αij j=i (3) + η2 where W represents the propagation bandwidth, gi is the link gain from node i to the nexthop node, Ri (pi , Ii ) is the information transmission rate obtained at the optimal power The transmission rate of information can be calculated using a power-interference model The optimization problem of the rate can be converted into optimization as follows max Ri (pi , Ii ) s.t.pi ≥ (4) It indicates that the maximum transmission rate supported by the sensor node is related to the disturbance suffered by the node at this time, that is, the rate Ri of node i is a function of transmitting power pi and interfered I i , and the effective interference Ii of node i is a function of pj , so the following rate model can be obtained: ⎛ ⎞ ⎜ Ri (pi , Ii ) = ln⎝1 + pi gi N j=1 pj gij αij j=i ⎟ ⎠ + (5) η2 The node will increase the transmission power to make up for the expected SINR that leads to more mutual severe interference It is necessary to determine its own transmit power according to the characteristics of the surrounding nodes Therefore, it can sense the state information of neighbor nodes that is not a local optimization problem The transmission rate is a function of the transmission power and active interference The interaction between the three is independent of each other In this way, a collaborating heuristic model composed of relevant factors can be constructed In the collaborating heuristic, it is emphasized that the final equilibrium result tends to the overall optimal value, and the strategy adopted by each node is the optimal response under the premise The strategy heuristic model is chosen as = p, f , each element is respectively: (1) Strategy space: P = {pi , p−i }(i = 1, …, n) is a strategy combination, pi is the strategy selection of node i, and p−i is the strategy selection of the remaining nodes (2) Utility function: f = {f (Ri (pi , I i ), E i )} denotes the network benefit when node i performs data communication with transmission power pi after algorithm iteration, and E i is the ratio of initial energy to residual energy of node f (Ri (pi , Ii ), Ei ) = c1 Ri (pi , Ii ) − c2 pi gi Ei e0 (i) = c1 Ri (pi , Ii ) − c2 pi gi ed (i) 334 J Yu et al ⎛ ⎞ pi gi N j=1 pj gij αij j=i ⎜ = c1 ln⎝1 + + η2 e0 (i) ⎟ ⎠ − c2 pi gi ed (i) (6) where c1 and c2 are utility weighting factors, e0 (i) is the initial energy of node I, ed (i) is the residual energy of node i It can be seen from the second term that when the residual energy ed (i) of the node is gradually reduced, the network utility shows a downward trend, so the transmission power should be appropriately reduced to delay the falling speed of the remaining energy The node i dynamically adjusts its own strategy by considering the surrounding node states comprehensively, and the optimal power strategy set when generating the maximum benefit is p = {p1 , …, pn }, and its element is expressed as follows p = arg maxf (Ri (pi , Ii ), Ei ) (7) WSN Energy Control Strategy with Collaborating Heuristic Iteration in the heuristic means that the same heuristic form is constantly appearing, and all participants decide the strategy based on current earnings and possible future returns The strategic repeated heuristic stipulates that each stage is a standard strategy heuristic In the actual judgment, the strategic repeated heuristic needs to satisfy: (1) The set of strategies of participant i belongs to a non-empty, closed, bounded convex set; (2) the utility function f = {f (Ri (pi , I i ), E i )} is a continuous function of pi , pi ∈[pimin , pimax ] is quasi-concave, it satisfies: ∂ f (Ri (pi , Ii ), Ei )