In narrowband Internet of Things (NB-IoT) networks, latency reduction is a significant issue especially regarding IoT applications with strict latency requirements. The signalling overhead constitutes the major factor of latency increase. 3GPP made several attempts to decrease the signalling overhead for massive machine type communications (mMTC) in NB-IoT networks. Among that attempts, early data transmission (EDT) is one of the latest efforts that reduces the signalling overhead by allowing data transmission without a connection establishment. Although EDT can support the transmission of multiple data packets besides one, it cannot deal with the randomness of data packet arrivals at buffered IoT devices. In this work, a new data transmission scheme is proposed that reduces signalling overhead by employing piggybacking. Specifically, the proposed scheme concerns buffered IoT devices, which transmit the first data packet of the buffer by participating in the contention-based random access (RA) and the following data packets on the reserved data channels without taking part in RA again, according to the proposed scheme. The effectiveness of the proposed scheme is evaluated with simulation experiments versus the number of IoT devices and mean interarrival time of data packets at each IoT device. Simulation results show that the proposed scheme reduces dropped packets, service delay and enhances throughput.

Energy conservation is crucial for clustered wireless sensor networks (WSNs) due to the bounded energy at sensor nodes. Energy conservation is achieved mainly through load balancing, which is realized with reclustering in frequent time intervals called rounds. Although in clustering the round duration is typically fixed throughout the network lifetime, the use of a dynamic round duration based on energy exhaustion at sensor nodes has also been proposed. Even though many attempts have been made to determine the round duration dynamically, all of them focus on WSNs. However, in mobile WSNs (MWSNs), apart from energy exhaustion, packet loss due to node mobility is also an important issue to be considered in the case of dynamic round duration. To investigate the problem of the calculation of the dynamic round duration in MWSNs, in this article, we evaluate the applicability of an existing recluster triggering scheme initially proposed for WSNs, which is adaptive to energy consumption at cluster heads (CHs). We also propose a new scheme, the lost ratio reclustering (LRRC), which provides adaptivity to the percentage of mobility-induced packet losses at each CH. Furthermore, the case of reclustering in fixed time intervals is also considered. Simulation results show the effectiveness of the LRRC scheme in increasing the packet delivery ratio (PDR) and reducing the clustering energy overhead.

Communication in Body Area Networks (BANs) involves extremely weak signals because of safety regulations. Human mobility adds one more layer of complexity as it has an effect on path loss depending on the activity. In this paper, we improve the quality of service (QoS) by searching for the lost nodes. Specifically, an Emergency Phase (EP) is added after RAP1 of IEEE 802.15.6-2012 superframe. The connected nodes transmit rescue beacons to reach distressed nodes, i.e. nodes that are disconnected. If a distressed node receives a rescue beacon, it participates to the current EP. The packets are buffered and relayed to the hub by the connected nodes. Our results show that when EP is enabled it is feasible to reach more nodes.

In wireless mobile sensor networks (WMSNs), the majority of clustering algorithms employ random waypoint (RWP) to consider mobility in nodes. In this work we evaluate the performance of clustering under RWP and random walk (RW) derivatives mobility models through simulations, since a particular mobility model cannot represent the mobility behavior of all nodes. The performance evaluation is conducted for two clustering approaches; the non-overlap and overlap clusters. In the first, a non-cluster-head node belongs exactly to one cluster, while in the second to more than one cluster. The latter is quite realistic and scarcely examined in the literature. Simulation results show that the performance of a clustering algorithm is strongly affected by the node mobility model selection and by many aspects of the model.