Ghahroudi Mahsa Sadeghi, Shahrabi Alireza, Boutaleb Tuleen
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.
Sensors (Basel). 2023 Sep 11;23(18):7797. doi: 10.3390/s23187797.
Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage.
许多动物群体在大规模上展现出显著的集体协调运动，这是个体分布式局部决策的结果。最近，通过利用最近邻规则对动物集体运动进行建模取得了进展，无需集中协调，从而在移动传感器网络（MSN）中开发了自部署算法，以实现不同应用所需的各种类型的覆盖。然而，与传感器移动以实现所需覆盖相关的能量消耗仍然是文献中报道的大多数算法的一个重大问题。本文提出了最近邻节点部署（NNND）算法，以在最小化能量消耗和增强容错能力的同时，有效地在给定区域提供全面覆盖。与其他依次移动传感器的算法不同，NNND通过采用多股传感器运动流来利用并行性的力量，每股运动流都指向该区域的不同部分。通过为每股运动流自适应选择一个领导者来维持每股运动流的内聚性，同时还确保避免碰撞。这些特性有助于最小化每股运动流内的行进距离，从而降低能量消耗。此外，NNND中使用多个领导者消除了单点故障的存在，从而增强了区域覆盖的容错能力。我们广泛的模拟研究结果表明，NNND不仅实现了更低的能量消耗，而且实现了更高百分比的k覆盖。
