In heterogeneous wireless sensor networks (HWSN), sensor nodes may fail due to running out of battery power or sudden damage. This causes HWSN being partitioned to a number of separate networks. Relay node placement is to add new relay nodes to partitioned HWSN such that the network recovers to wireless communication. Energy efficient routing in HWSN is useful for prolonging network lifetime and energy conservation. This paper attempts to solve relay node placement and energy efficient routing problems for HWSN. Both problems have seldom been studied together in the literature. This paper first constructs a mathematical model for both problems. For relay node placement problem, it is assumed that HWSN contains unreachable area, where sensor nodes could not be placed. For energy efficient routing, it is transformed to path length of wireless communication. As the problem is non-deterministic polynomial (NP) hard, a heuristic method called whale optimizer is used. The paper studies the effect of whale optimizer method with three adaptive schemes. Numerical simulations are done to test the proposed method for HWSN. The analysis and discussion show that the proposed method is useful to address NP hard relay placement and energy saving problem for HWSN.
The theory of compressive sensing is briefly introduced, and some construction methods for measurement matrix are deduced. A measurement matrix based on Kronecker product is devised, and it has been proved in mathematical proof. Numerical simulations on 2-D image verify that the proposed measurement matrix has better performance in storage space, construction time, and image reconstruction effect when compared with commonly used matrices in compressive sensing. This novel measurement matrix offers great potential for hardware implementation of compressive sensing in image and high-dimensional signal.
The collaborative relay network plays an important role in a smart grid application thanks to its high efficiency. However, energy and bandwidth management need to be further improved to extend network life cycle and reduce potential network segmentation. Aiming to balance the network energy consumption, this paper proposes an adaptive relay node selection algorithm based on the opportunity (OAR). In OAR, the potential relay nodes are chosen to act as candidates based on bit error rate (BER) estimation, and then, the relay is adaptively and opportunistically determined for packet forwarding. Residual energy is considered to avoid fast energy exhaustion for some certain nodes. The simulation results show that the proposed OAR algorithm improves the network performance by deferring the earliest death time of the nodes and extending the network life cycle.
When designing and deploying wireless sensor network (WSN), sensor node deployment is one of the crucial issues for WSN providing high quality of service. Sensor node deployment could also affect network coverage, network connectivity, network lifetime, and energy consumption. This paper attempts to solve sensor node deployment problem in WSN. A mathematical model is constructed by considering coverage maximization, deployment strategy and other constraints. As the problem is non-deterministic polynomial (NP) complete, a new method called sparrow search algorithm (SSA) is used. A novel population structure is proposed to improve SSA to solve the problem. Two populations with central symmetry property are created in initialization. Crossover operator is used to exchange information of the two populations. The computational complexity of the improved SSA algorithm is also discussed. Numerical simulations are performed to solve the deployment problem in WSN. The improved SSA algorithm is tested and compared with other algorithms. The results show that the proposed method is useful to address NP complete sensor node deployment problem in WSN.
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