Machine learning algorithms for wireless sensor networks: A survey

Many recent wireless sensor network (WSN) routing protocols are enhancements to address specific issues with the “low-energy adaptive clustering hierarchy” (LEACH) protocol. Since the performance of LEACH deteriorates sharply with increasing network size, the challenge for new WSN protocols is to extend the network lifespan while maintaining high scalability. This paper introduces an energy-efficient clustering and hierarchical routing algorithm named energy-efficient scalable routing algorithm (EESRA). The goal of the proposed algorithm is to extend the network lifespan despite an increase in network size. The algorithm adopts a three-layer hierarchy to minimize the cluster heads’ load and randomize the selection of cluster heads. Moreover, EESRA uses multi-hop transmissions for intra-cluster communications to implement a hybrid WSN MAC protocol. This paper compares EESRA against other WSN routing protocols in terms of network performance with respect to changes in the network size. The simulation results show that EESRA outperforms the benchmarked protocols in terms of load balancing and energy efficiency on large-scale WSNs.

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EESRA: Energy Efficient Scalable Routing Algorithm for Wireless Sensor Networks

Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm

Clustering sensor nodes is an effective method in designing routing algorithms for Wireless Sensor Networks (WSNs), which improves network lifetime and energy efficiency. In clustered WSNs, cluster heads are the key nodes, they need to perform more tasks, so they consume more energy. Therefore, it is an important problem to select the optimal cluster heads. In this paper, we propose a clustering algorithm that selects cluster heads using an improved artificial bee colony (ABC) algorithm. Based on the standard ABC algorithm, an efficient improved ABC algorithm is proposed, and then the network cluster head energy, cluster head density, cluster head location and other similar factors are introduced into the improved ABC algorithm theory to solve the clustering problem in WSNs. In the network initialization period, all nodes have the same energy level, the improved ABC algorithm is used to optimize fuzzy C-means clustering to find the optimal clustering method. We also propose an energy-efficient routing algorithm based on an improved ant colony optimization for routing between the cluster heads and the base station. In order to improve energy efficiency and further improve network throughput, in the stable transmission phase, we introduce a polling control mechanism based on busy/idle nodes into intra-cluster communication. The performance of the proposed protocol is evaluated in several different scenarios. The simulation results show that the proposed protocol has a better performance compared to a number of recent similar protocols.

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An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks

In this paper, a new Class Topper Optimization (CTO) algorithm is proposed. The optimization algorithm is inspired from the learning intelligence of students in a class. The algorithm is population based search algorithm. In this approach, solution is converging towards the best solution. This may lead to a global best solution. To verify the performance of the algorithm, a clustering problem is considered. Five standard data sets are considered for real time validation. The analysis shows that the proposed algorithm performs very well compared to various well known existing heuristic or meta-heuristic optimization algorithms.

A New Class Topper Optimization Algorithm with an Application to Data Clustering

How to make efficient data routing in energy constrained wireless sensor networks (WSNs) is one of the key points. In order to find the optimal path of data transmission in the WSNs, a new routing algorithm based on ant colony algorithm is proposed. Using the improved heuristic function and considering the node communication transmission distance, transmission direction, and residual energy, an optimal path from the source node to the destination node can be found. Thus, the network energy consumption is reduced and the network lifetime is prolonged. Simulation results show that new ant algorithm can effectively save the energy of nodes and prolong the network lifetime.

An Improved Routing Algorithm Based on Ant Colony Optimization in Wireless Sensor Networks

Adaptive Decomposition-Based Evolutionary Algorithm for Many-Objective Optimization with Two-Stage Dual-Density Judgment

Uplink performance analysis in D2D-enabled cellular networks with clustered users

In the asynchronous massive machine-type communications (mMTC) supported by massive MIMO based grant-free random access, where all active devices asynchronously transmit the pilot sequences to the BS, it necessitates the joint activity detection and channel estimation (JADCE) with imperfect synchronization. In this paper, in order to achieve a good balance between the number of pilots and the correlation property of pilots in the presence of time delay, we propose a partially orthogonal pilot sequence (POPS) consisting of multiple modulated chirp symbols. The robustness of the proposed pilots to time asynchrony allows the BS to detect the device activity before the time delay estimation. In sequel, taking advantage of the energy convergence feature of the pilots in the FFT domain after demodulation, we propose a selective interference elimination aided orthogonal matching pursuit (SIE-OMP) algorithm to solve the formulated JADCE problem with a reduced-size set of candidate pilots. The complexity analysis and numerical results validate that, compared with the testbench schemes, the proposed scheme can achieve a better JADCE performance with a lower computational complexity. 

Yongjun Sun

Papers

An Improved Routing Algorithm Based on Ant Colony Optimization in Wireless Sensor Networks

Adaptive Decomposition-Based Evolutionary Algorithm for Many-Objective Optimization with Two-Stage Dual-Density Judgment

Uplink performance analysis in D2D-enabled cellular networks with clustered users

Uplink performance analysis in D2D-enabled cellular networks with clustered users

Low-Complexity Joint Activity Detection and Channel Estimation with Partially Orthogonal Pilot for Asynchronous Massive Access