Energy efficient data collection with minimum delay is very essential for IoT applications using sensors attached to Low Rate Wireless Personal Area Networks (LR-WPAN). The nature and position of the data collection and processing point, termed as Sink, plays a vital role in the data reliability, delay, and network lifetime. The static sink causes hot-spot problem to occur in Wireless Sensor Network (WSN), since the nodes nearer to the sink may die quickly. The Mobile Data Collection Agent (MDCA), commonly known as Mobile Sink (MS), can be used to mitigate this problem. A two level clustering scheme with an optimal data collection strategy is proposed in this work. In the first level, hierarchical clusters are formed and the Cluster Heads (CHs) are selected based on the residual energy and connectivity of the devices. The CHs are partitioned into disjoint sets and each set is represented by a Rendezvous Point (RP) that covers every element in the set. In the second level clustering, a deterministic approach is used for the RP identification followed by a meta-heuristic approach known as Gravitational Search Algorithm (GSA) for RP optimization. Finally, the MDCA collects data by visiting RPs along the path obtained using Simulated Annealing (SA). The proposed method is compared with Particle Swarm Optimization based path finding, SPT-GDA, and BR-CTR algorithms. The algorithms are analyzed for number of CHs, RPs, path length, Average Residual Energy, and Network Lifetime in LR-WPAN.

对于使用连接到低速率无线个人局域网（LR-WPAN）的传感器的物联网应用而言，以最小的延迟实现高效的数据收集至关重要。数据收集和处理点的性质和位置，称为接收器，在数据可靠性，延迟和网络寿命方面起着至关重要的作用。静态接收器会导致无线传感器网络（WSN）中出现热点问题，因为更靠近接收器的节点可能会很快死亡。移动数据收集代理（MDCA），通常称为移动接收器（MS），可用于缓解此问题。在这项工作中，提出了一种具有最佳数据收集策略的两级聚类方案。在第一层中，形成了层次集群，并根据设备的剩余能量和连接性选择了集群头（CH）。CH被划分为不相交的集合，并且每个集合都由一个集合点（RP）表示，该集合点涵盖了集合中的每个元素。在第二层聚类中 确定性方法用于RP识别，然后使用称为启发搜索算法（GSA）的元启发式方法进行RP优化。最后，MDCA通过访问沿使用模拟退火（SA）获得的路径的RP来收集数据。将该方法与基于粒子群算法的路径查找，SPT-GDA和BR-CTR算法进行了比较。针对LR-WPAN中的CH，RP，路径长度，平均剩余能量和网络生存时间对算法进行了分析。