Wireless sensor networks (WSNs) are a collection of many tiny sensor devices termed sensor nodes or motes that form connected networks once deployed. Because sensor nodes are relatively cheap, a typical sensor network normally consists of hundreds, if not thousands, of sensor nodes, with each capable of sensing data from its environment and relaying the sensed data through other sensor nodes, in an ad-hoc fashion to a centralized location or sink [19]. Of late, there have been much research interests in WSNs as they pose many interesting challenges in designing optimal WSN frameworks for many different applications. WSNs can be used as a tool in an array of different industries such as agriculture, marine, military, medical, and the focus of our research, manufacturing. In manufacturing, the use of WSNs can reduce costs associated with machine faults and network maintenance, prevent safety hazards, and improve production quality, all at a low deployment cost [23,8,33,13].
In most contexts of WSN, the design framework must optimize all performance metrics by sustaining energy-efficiency, memory-efficiency, self-organization, and network performance; this is no exception in manufacturing. Sensor nodes are battery powered devices and replacement or re-charge of a sensor node's battery is often not feasible due to its low cost, hence energy resource is limited. To extend network lifetime, each sensor node in the network must conserve as much energy as possible by duty cycling. Since the radio transceiver of a sensor node is the main energy consumer [24], a sensor node should power down its radio transceiver when it is not involved in any communication. Sensor nodes must also be able to self-organize, such that they must be able to self-start, self-configure, and self-heal; all sensor nodes must collaborate with each other to form a connected network, construct new communication links when new nodes are introduced in the network, destruct communication links when existing nodes deplete their battery power, and be fault-tolerant such that they must be able to detect, repair, and/or re-establish failed communication links, all without external configuration. To achieve optimal network performance, data throughput must be maximized, end-to-end latency of data transmission must be minimized, fairness among nodes sustained, and bandwidth utilization maximized [17]. An optimal network performance usually guarantees a certain level of Quality of Service (QoS). Because sensor nodes are low-cost devices, they have very limited memory capacity [4]. As such, they must not perform excessive computation and must avoid storing non-essential or overhead data in memory where possible.
The framework for WSNs in manufacturing is different to that from any other general WSN framework. In most manufacturing plants, the conditions are harsher in that large machineries are typically made of metals. For this reason, the environment may be prone to signal fading, interference produced from machine noise, and complete signal obstruction [31,16,12]. Multipath waves emitted by the sensor nodes that go through attenuation, reflection, diffraction, and transmission in a typical industrial plant may result in signal fading [32]. As a signal fades, the transmitted signal will degrade by the time it reaches a receiving node, causing the data to be corrupted. White noise generated by acoustical materials, such as vibrating steel panels, power cables, and fans, in the industrial plant may also interfere with the frequency with which the sensor nodes are communicating [32], causing the transmitted signal to be corrupted. These two problems are not unfamiliar terrains in WSNs as other general WSN frameworks normally encounter the same problems in dealing with signal fading and interference. However, there is an additional problem posed in manufacturing; machineries that are made of metals can completely obstruct radio signals emitted by sensor nodes as electromagnetic waves cannot penetrate metal plates [18]. In the case of signal fading and interference, a sensor node is able to detect these disruptions by either listening to the medium for collisions or performing integrity check (e.g. checksum) upon receiving a signal. On the other hand, the loss of a transmitted signal due to signal obstruction will be transparent to the sensor node. As such, we need to take into consideration these problems in designing a WSN framework suitable for manufacturing environment while sustaining the performance metrics mentioned previously.