Wave: a distributed scheduling algorithm for convergecast in IEEE 802.15.4e TSCH networks

Abstract

The IEEE 802.15.4e MAC amendment has been proposed to meet the requirements of industrial applications. Using slotted medium access with channel hopping, the MAC layer orchestrates the medium accesses of nodes according to a given schedule. Nevertheless, this amendment does not specify how this schedule is computed. The purpose of this paper is to propose a distributed joint time slot and channel assignment, called Wave, for data gathering in low-power lossy networks. This schedule targets minimised data convergecast delays by reducing the total number of slots in the schedule. Moreover, Wave ensures the absence of conflicting transmissions in the schedule provided. In such a schedule, a node is awake only during its transmission slots and those of its children in the convergecast routing graph. Thus, energy efficiency is ensured. In this paper, we present Wave with its properties (e.g. support of heterogeneous traffic, support of a sink equipped with multiple interfaces, worst case delays and buffer size) and compare it with a centralised scheduling algorithm like TMCP and a distributed one like DeTAS. Simulation results show the good performance of Wave compared with TMCP. Because in an industrial environment, several routing graphs can coexist, we study how Wave supports this coexistence. Copyright

A Distributed Scheduling Algorithm for IEEE 802.15.4e Networks

“…Its simulation results showed that the transmission delay time, duty cycle, and power consumption, are all reduced. Distributed scheduling is proposed in [16][17] [18]. The DeTAS is a similar method to the TASA, which also prioritizes nodes by the packet traffic flow to schedule time slots.…”

Autonomous and traffic-aware scheduling for TSCH networks

“…The first option is to assess traffic load at each node as the sum of traffic it generates and the traffic generated by the nodes belonging to its sub-tree, such as in [15,16]. This solution leads to laborious neighbor-to-neighbor negotiation as each node has to collect information of traffic load from its children and then forwards the total traffic load information to its parent.…”

“…Several scheduling algorithms have been proposed for TSCH networks. They can be broadly classified as either centralized [12][13][14] or distributed [15][16][17][18] algorithms.…”

“…Wave [16] is another example of distributed scheduling algorithms for TSCH, were the schedule is build by computing a series of waves. The sink node sends a message to its children to trigger the computation of the first wave.…”

“…Therefore, several scheduling algorithms have been proposed for TSCH networks. They can be broadly classified as either centralized [12][13][14] or distributed [15][16][17][18] algorithms. Both centralized and distributed scheduling techniques involve additional communication overhead: each node is supposed to communicate with its neighbors or with a central entity to exchange network and traffic information in addition to scheduling information.…”