# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 1 Introduction  
Wireless sensor networks (WSNs) are composed of inexpensive microprocessors with wireless communication, computing, storage capabilities and the ability to perceive the surroundings [1; 2; 3; 4]. They are emerging as promising platforms that enable a wide range of applications in numerous areas such as smart cities [5; 6], traffic monitoring [7], factory automation control [8], public facilities monitoring [9; 10], human health monitoring [11; 12], weather monitoring, wildlife protection and military applications [13; 14; 15; 16; 17; 18; 19]. With the development of micro-processing technology, sensor nodes are not only becoming more and more powerful, but also have a smaller volume and cheaper price. Consequently, the prospects of sensor nodes are more and more promising [20; 21; 22], such as forming a new type of edge computing and edge network [5; 23]. Besides, they become an important part of the Internet of Things (IoT) [2; 12; 18; 24; 25], which enables different Internet-connected objects to exchange data. Combined with cloud networks [4; 15; 26], Sensor-Cloud Networks (SCNs) can also be made up of those sensors. Furthermore, there is a growing interest in forming social networks [24; 27; 28] with mobile sensor devices like mobile phones, which can deeply change human life. Additionally, collecting data from monitored objects [29; 30; 31] is one of the important applications of wireless sensors, in which sensor nodes form a dynamic topological network by self-organization, and transmit sensed data to a control center (called sink) by multi-hop routing [32; 33; 34]. Then the sink performs the corresponding control action on the monitored or controlled objects so as to achieve some specific functions [35; 36; 37; 38]. As well known, there are numerous applications requiring high transmission reliability for data routing [29; 36]. The transmission reliability is particularly important for mission-critical applications such as remote patient monitoring, battlefield monitoring, monitoring of disaster-struck regions, home automation, and tracking of chemical and explosive agents. These applications are loss-and-delay sensitive and therefore reliable and timely delivery of information is critical [39; 40; 41]. In addition to loss-and-delay sensitivity, the energy efficiency is also of concern because wireless sensor nodes have limited battery capacity [42; 43]. However, because of low power radios, noise, multipath interference and external interference [44; 45], wireless communication links in WSNs are typically volatile, unreliable, and error-prone [37]. Unfortunately, bad links in a routing path may lead to unpredictable data packet loss. Even worse, the sink it is more likely to make wrong decisions without sufficient data. In addition, even if a retransmission policy is applied, it may cause increased end-to-end delay, which may further bring about system loss due to unresponsiveness to emergencies in a timely way and more energy consumption, thus reducing the network lifetime. There are already a number of reliable, low delay and energy efficient routing strategies for WSNs [29; 36; 46]. The one-hop transmission reliability of data between two sensor nodes mainly refers to the probability that the receiver node successfully receives data after the sender node sends packets. Considering that in WSNs, the data generated by the source node is usually routed to the sink by multiple hops, therefore end-to-end reliability indicates the probability that the sink successfully receives the data packet after passing through multiple hops, hence end-to-end reliability is the product of all the one-hop transmission reliabilities on the routing path [47]. Accordingly, if one-hop transmission reliability is determined, end-to-end reliability will decrease with the growth of the number of routing hops. As a consequence, in order to maintain high reliability in a long route, it is necessary to maintain a high one-hop transmission reliability [29; 47].