Indoor positioning technology is one of the cornerstones for many services in Internet-of-Things (IoT) systems. However, the existing indoor positioning systems are still suffering from several issues, such as unstable positioning accuracy, a high system complexity, and a high deployment cost. To tackle these issues, this article presents a Particle Filter-based Indoor Positioning System (PFIPS) that can localize and track a tag that broadcasts Bluetooth Low Energy (BLE) beacon messages to BLE receivers. The proposed PFIPS uses a Kalman Filter to preprocess collected Received Signal Strength Indication (RSSI) information in order to smooth the fluctuated RSSI data. It also designs an effective Particle Filter (PF) to approximate the location of a tag, which gradually reduces the location uncertainties in a Gaussian belief space. To show the applicability of our PFIPS, we have developed PFIPS in a testbed based on commercial off-the-shelf (COTS) devices. Through intensive simulations and experiments, our experiment results show that our PFIPS outperforms the legacy indoor positioning systems in terms of location accuracy by 24.1% and achieves median accuracy of 1.16 m.

This paper proposes an evolved Self-Adaptive Interactive Navigation Tool (SAINT+) to reduce the delivery time of emergency services and to improve navigation efficiency for the vehicles influenced by accidents. To the best of our knowledge, SAINT+ is the first attempt to optimize the delivery of emergency services as well as the navigation routes of vehicles around accident areas. Based on the congestion contribution model of SAINT and aggregated information from vehicles in the vehicular cloud, we propose a virtual path reservation strategy for emergency vehicles to guarantee a fast emergency service delivery. We also develop an accident area protection scheme based on an adjusted congestion contribution matrix and protection zones to evacuate vehicles in the accident area. To further reduce travel delay of neighbor vehicles in the accident area, we also present a dynamic traffic flow control model. Through extensive simulations with a real-world map, SAINT+ outperforms other state-of-the-art schemes for the travel delay of emergency vehicles. In scenarios with a high vehicle density, SAINT+ reduces the travel delay of emergency vehicles by 42.2%.

In this paper, we propose a spatio-temporal coordination-based media access control (STMAC) protocol for efficiently sharing driving safety information in urban vehicular networks. STMAC exploits a unique spatio-temporal feature characterized from a geometric relation among vehicles to form a line-of-collision graph, which shows the relationship among vehicles that may collide with each other. Based on this graph, we propose a contention-free channel access scheme to exchange safety messages simultaneously by employing directional antenna and transmission power control. Based on an urban road layout, we propose an optimized contention period schedule by considering the arrival rate of vehicles at an intersection in the communication range of a road-side unit to reduce vehicle registration time. Using theoretical analysis and extensive simulations, it is shown that STMAC outperforms legacy MAC protocols especially in a traffic congestion scenario. In the congestion case, STMAC can reduce the average superframe duration by 66.7%, packet end-to-end delay by 68.3%, and packet loss ratio by 88% in comparison with the existing MAC protocol for vehicle-to-infrastructure communication, based on the IEEE 802.11p.

Vehicular networks have emerged as a promising means to mitigate safety hazards in modern transportation systems. On highways, emergency situations associated with vehicles necessitate a reliable media access control (MAC) protocol that can provide timely warnings of possible vehicle collisions. In this paper, we present an edge-assisted cluster-based MAC protocol (ECMAC) for packet dissemination in software-defined vehicular networks. To reduce the control messaging overhead for clustering, ECMAC separates the cluster control plane (i.e., managing cluster formation) from the data plane (i.e., actual data transmission and forwarding) by using a software-defined network controller in a cellular network edge server. For transmitting packets, we design a time-division multiple access (TDMA) schedule algorithm to guarantee a high reliability and a low latency. The TDMA schedule in ECMAC is determined by a joint optimization process in the cellular edge, which is formulated as a binary integer linear programming problem and solved by a heuristic approach based on the divide-and-conquer paradigm. This joint optimization process minimizes the signal interference by jointly considering channel assignment and time slot allocation, thereby ensuring reliable communication. Through extensive simulations, our performance results show that ECMAC improves the successful delivery ratio of emergency packets by at least 25 %, compared with state-of-the-art approaches.