Energy efficient multi-hop path in wireless sensor networks using an enhanced genetic algorithm

Energy-Efficient and Load-Balanced Clustering Routing Protocol for Wireless Sensor Networks Using a Chaotic Genetic Algorithm

“…As mentioned above [1,2], clustering routing protocols have been widely used to improve energy efficiency and to extend the network lifetime. Moreover, heuristics based methods are more likely to get the optimal solutions than traditional methods [8][9][10]13,17,[20][21][22][23] due to their capabilities of adaptivity on network dynamics and uncertainties, and excellent search ability. By simulating the evolution process of biological populations in nature, genetic algorithms with low computational complexity can not only directly operate on the objects, automatically obtain and guide the optimized search space, but also adaptively adjust the search direction, quickly converge, and find the best global solution in the end.…”

“…Clustering usually consists of CHs selection and clusters formation. Many approaches have been provided for CHs selection, which can be categorized into probability based [4][5][6][11][12][13][14], weight based [7,[15][16][17] and heuristics based [9,10,[18][19][20] approaches. The nodes are selected as CHs when their threshold values are less than a random assigned real number between 0 and 1 in probability based approaches.…”

“…Routing is used to find the optimal routing path for each CH because the CMs only can communicate with their corresponding CHs. When a CH receives all the data from its CMs, it fuses and forwards the data to the BS directly in single-hop mode [3][4][5][6]19,22,23] or indirectly in multi-hop mode [7][8][9][10][11][12][13][14][15][16][17][18]24]. Usually, communication between CHs and BS in single hop mode may expend excessive energy when the CH appears far away from the BS and undoubtedly reduce the scalability of the network.…”

“…Thus, data forwarding in multi-hop mode gradually becomes a common way for routing by selecting proper relay nodes to the BS so as to reduce energy consumption [25]. Similar to cluster selection, weight-based [7,11,18] and heuristics based [8][9][10]13,16,17,20,21,26] approaches are used for finding routing paths. Weight based on residual energy of the nexthop CHs [7], distance to the next-hop CHs [18], and so on [11] is used to select the nodes with higher weight values as the next-hop CHs.…”

“…Similarly, local decision as well as neglecting uncertainties and dynamics of the network makes it hard to find the optimal routing paths. Hence, fuzzy logic [9], particle swarm optimization [8], ant colony optimization [20,26], genetic algorithm [10,13,17,20] are utilized to find the optimal routing paths for each CH so as to achieve balanced energy consumption of CHs and increased network lifetime. Moreover, they can also alleviate hot spot problem caused by hop-by-hop routing to some extend as other unequal clustering routing methods by adjusting cluster size, selecting vice CHs, and controlling hop counts and so on [12,15,24,27].…”