Generally, various traffic requirements in wireless sensor network are mostly dependent on specific application types, that is, event-driven, continuous, and query-driven types. In these applications, real-time delivery is one of the important research challenges. However, due to harsh networking environment around a node, many researchers usually take different approach from conventional networks. In order to discuss and analyze the advantage or disadvantage of these approaches, some comprehensive survey literatures were published; however they are either out of date or compiled for communication protocols on single layer. Based on this deficiency, in this paper, we present the up-to-date research approaches and discuss the important features related to real-time communications in wireless sensor networks. As for grouping, we categorize the approaches into hard, soft, and firm real-time model. Furthermore, in all these categories, research has been focused on MAC and scheduling and routing according to research area or objective in second level. Finally, the article also suggests potential directions for future research in the field.

Traditionally, geocasting has been known as the appropriate scheme for providing effective data dissemination from a source to all nodes in a geographically restricted region. However, since the geocasting typically assumes the restricted region is stationary, it is hard to directly adopt the traditional geocasting in order to offer effective data delivery to mobile sink groups that have geographically collective mobility. Hence, this letter proposes a novel geocasting, called M-Geocasting (Mobile Geocasting). M-Geocasting provides the representative location information of a sink group to sources. The location information contains information with respect to a restricted region in which all member sinks of the group exist. A source disseminates data to the closest node in the region; then, the node restrictedly floods the data only within the region. Also, to support local movement of member sinks toward out of scope of the region, some nodes on boundary of the region maintain the data and offer it to member sinks out of scope of the region.

Due to the lack of dependency on beacon messages for location exchange, the beaconless geographic routing protocol has attracted considerable attention from the research community. However, existing beaconless geographic routing protocols are likely to generate duplicated data packets when multiple winners in the greedy area are selected. Furthermore, these protocols are designed for a uniform sensor field, so they cannot be directly applied to practical irregular sensor fields with partial voids. To prevent the failure of finding a forwarding node and to remove unnecessary duplication, in this paper, we propose a region-based collision avoidance beaconless geographic routing protocol to increase forwarding opportunities for randomly-deployed sensor networks. By employing different contention priorities into the mutually-communicable nodes and the rest of the nodes in the greedy area, every neighbor node in the greedy area can be used for data forwarding without any packet duplication. Moreover, simulation results are given to demonstrate the increased packet delivery ratio and shorten end-to-end delay, rather than well-referred comparative protocols.

This article presents a future-driven routing architecture for Internet of Things (IoT). This IoT is a novel concept involving a new concept regarding a set of things with the same routing and service polices, denoted by an autonomous system of things (ASoT). In IoT, an ASoT would be connected not only to the others but legacy autonomous systems (ASs) for the Internet in a wide variety of scenarios. Hence, this article firstly addresses classification of diverse features of ASoTs, and then explores new challenges especially on inter-domain routing.

This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.