In the “network-as-a-service” paradigm, network operators have a strong need to know the performance of critical paths running services to the users. Network tomography is an attractive methodology for inferring internal network characteristics from end-to-end measurements between monitors. Motivated by previous results that uniquely identifying the path metrics can require a large number of monitors, we focus on calculating the performance bounds of a set of interesting paths, i.e., bound-based network tomography for interesting paths. We present an efficient solution to obtain the tightest upper and lower bounds of all interesting paths in an arbitrary network with a given set of end-to-end measurements. Based on this solution, we further develop an algorithm to place new monitors over existing ones such that the bounds of interesting paths can be maximally tightened. We formally prove the effectiveness of the proposed algorithms. We implement the algorithms and conduct extensive experiments on real ISP topologies. Compared with state-of-the-art approaches, our algorithms achieve up to$1.2\sim 1.9$times more reduction on the bound interval lengths of all interesting paths and use up to 50.4%~62.5% fewer monitors in various network settings.

Interoperability among a vast number of heterogeneous IoT nodes is a key issue. However, the communication among IoT nodes does not fully interoperate to date. The underlying reason is the lack of a lightweight and unified network architecture for IoT nodes having different radio technologies. In this paper, we design and implement TinyNet, a lightweight, modular, and unified network architecture for representative low-power radio technologies including 802.15.4, BLE, and LoRa. The modular architecture of TinyNet allows us to simplify the creation of new protocols by selecting specific modules in TinyNet. We implement TinyNet on realistic IoT nodes including TI CC2650 and Heltec IoT LoRa nodes. We perform extensive evaluations. Results show that TinyNet (1) allows interoperability at or above the network layer; (2) allows code reuse for multi-protocol co-existence and simplifies new protocols design by module composition; (3) has a small code size and memory footprint.

Network tomography is an attractive methodology for inferring internal network states from accumulated path measurements between pairs of monitors. Motivated by previous results that identifying all link metrics can require a large number of monitors, we focus on calculating the performance bounds of a set of interesting links, i.e., bound-based network tomography. We develop an efficient solution to obtain the tightest upper bounds and lower bounds of all interesting links in an arbitrary network with a given set of end-to-end path measurements. Based on this solution, we further propose an algorithm to place new monitors over existing ones such that the bounds of interesting links can be maximally tightened. We theoretically prove the effectiveness of the proposed algorithms. We implement the algorithms and conduct extensive experiments based on real network topologies. Compared with state-of-the- art approaches, our algorithms can achieve 2.2~3.1 times more reduction on the bound interval lengths of all interesting links and reduce the number of placed monitors significantly in various network settings.

Multihop low-power wireless communication is a key feature of wireless sensor networks (WSNs). Although with a lot of efforts, the performance of multihop WSNs in practical deployment could still be unpredictable and unsatisfactory, such as high packet transmission latency and short network lifetime. Narrowband Internet of Things (NB-IoT) is an emerging cellular technology for providing wide-area coverage. In this article, we investigate the possibilities of deploying 802.15.4 and NB-IoT equipped sink nodes (i.e., gateways) into existing WSNs to achieve the predictable and improved performance in terms of end-to-end reliability, end-to-end latency, and network lifetime. To calculate these key network-wide metrics, we propose a hybrid analytical model for 802.15.4 and NB-IoT traffic, taking the new features of NB-IoT into account. Based on the established model, we formally consider two types of fundamental problems, i.e., the minimum gateway deployment and the optimal κ-gateway deployment. We implement our framework and extensively evaluate it in the realistic as well as synthetic WSNs. Results demonstrate that our approach can provide significant performance gains, e.g., the network lifetime can be extended by 37.4%-80.3% by deploying only one sink node. Compared with a state-of-the-art method, our approach improves the network lifetime by 12.6%-31.8%.

Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is essential for various network operations, such as network monitoring, load balancing, and failure diagnosis. Given a set of interesting links and the changing topologies of a dynamic network, we study the problem of calculating the metrics of these interesting links by end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. We propose MAPLink, an algorithm that assigns a number of nodes as monitors to solve this tomography problem. As the first algorithm to solve the preferential link tomography problem in dynamic networks, MAPLink guarantees that the assigned monitors can calculate the metrics of all interesting links in each possible topology of a dynamic network. We formally prove the above property of MAPLink based on graph theory. We implement MAPLink and evaluate its performance using two real-world dynamic networks, including a vehicular network and a sensor network, both with constantly changing topologies due to node mobility or wireless dynamics. Results show that MAPLink achieves significant better performance compared with four baseline solutions in both of these two dynamic networks.

With the development of LPWA (Low Power Wide Area)technology, the emerging NB-IoT (Narrowband Internet of Things)has attracted much attention and enabled a wide range of applications. An uplink of NB-IoT is a link from a user equipment (UE)to a base station (BS). Uplink transmission is a key component of NB-IoT, accomplishing the sensor data collection task for many applications. However, the performance of uplink transmission has not been rigorously analyzed in the current literature, while uplink performance degradation like long latency could be harmful to many applications with strict uplink performance requirements. In this work, we show a way of mathematically analyzing the performance of uplink transmission for NB-IoT systems, concerning the transmission latency and transmission reliability. Our model is accurate with consideration of the protocol details and the new features of NB-IoT, including link quality, packet size, channel access contention, and etc. We validate the analytical results through detailed simulations. Results show that our analytical model can achieve 83% accuracy for latency calculation and 96% accuracy for reliability calculation. Moreover, we demonstrate that the analytical results can be used to aid protocol design for performance optimization, e.g., repetition number tuning for reducing the transmission latency.

Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is essential for various network operations, such as network monitoring, load balancing, and failure diagnosis. Given a set of interesting links and the changing topologies of a dynamic network, we study the problem of calculating the link metrics of these links by end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. We propose MAPLink, an algorithm that assigns a number of nodes as monitors to solve this tomography problem. As the first algorithm to solve the preferential link tomography problem in dynamic networks, MAPLink guarantees that the assigned monitors can calculate the link metrics of all interesting links for all topologies of the dynamic network. We formally prove the above property of MAPLink based on graph theory. We implement MAPLink and evaluate its performance using two real-world dynamic networks, including a vehicular network and a sensor network, both with changing topologies due to node mobility or wireless dynamics. Results show that MAPLink achieves significant better performance compared with three baseline methods in both of the two dynamic networks.